Telco energy consumption – a path to a greener future?

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To my friend Rudolf van der Berg this story is not about how volumetric demand (bytes or bits) results in increased energy consumption (W·h). That notion is silly, as we both “violently” agree on ;-). I recommend that readers also check out Rudolf’s wonderful presentation, “Energy Consumption of the Internet (May 2023),” which he delivered at the RIPE86 student event this year in 2023.

Recently, I had the privilege to watch a presentation by a seasoned executive talk about what his telco company is doing for the environment regarding sustainability and CO2 reduction in general. I think the company is doing something innovative beyond compensating shortfalls with buying certificates and (mis)use of green energy resources.

They replace (reasonably) aggressively their copper infrastructure (country stat for 2022: ~90% of HH/~16% subscriptions) with green sustainable fiber (country stat for 2022: ~78%/~60%). This is an obvious strategy that results in a quantum leap in customer experience potential and helps reduce overall energy consumption resulting from operating the ancient copper network.

Missing a bit imo, was the consideration of and the opportunity to phase out the HFC network (country stat for 2022: ~70%/~60%) and reduce the current HFC+Fibre overbuild of 1.45 and, of course, reduce the energy consumption and operational costs (and complexity) of operating two fixed broadband technologies (3 if we include the copper). However, maybe understandably enough, substantial investments have been made in upgrading to Docsis 3.1. An investment that possibly still is somewhat removed from having been written off.

The “wtf-moment” (in an otherwise very pleasantly and agreeable session) came when the speaker alluded that as part of their sustainability and CO2 reduction strategy, the telco was busy migrating from 4G LTE to 5G with the reasoning that 5G is 90% more energy efficient compared to 4G.

Firstly, it is correct that 5G is (in apples-for-apples comparisons!) ca. 90% more efficient in delivering a single bit compared to 4G. The metric we use is Joules-per-bit or Watts-seconds-per-bit. It is also not uncommon at all to experience Telco executives hinting at the relative greenness of 5G (it is, in my opinion, decidedly not a green broadband communications technology … ).

Secondly, so what! Should we really care about relative energy consumption? After all, we pay for absolute energy consumption, not for whatever relativized measure of consumed energy.

I think I know the answer from the CFO and the in-the-know investors.

If the absolute energy consumption of 5G is higher than that of 4G, I will (most likely) have higher operational costs attributed to that increased power consumption with 5G. If I am not in an apples-for-apples situation, which rarely is the case, and I am anyway really not in, the 5G technology requires substantially more power to provide for new requirements and specifications. I will be worse off regarding the associated cost in absolute terms of money. Unless I also have a higher revenue associated with 5G, I am economically worse off than I was with the older technology.

Having higher information-related energy efficiency in cellular communications systems is a feature of the essential requirement of increasingly better spectral efficiency all else being equal. It does not guarantee that, in absolute monetary terms, a Telco will be better off … far from it!

THE ENERGY OF DELIVERING A BIT.

Energy, which I choose to represent in Joules, is equal to the Power (in Watt or W) that I need to consume per time-unit for a given output unit (e.g., a bit) times the unit of time (e.g., a second) it took to provide the unit.

Take a 4G LTE base station that consumes ca. 5.0kW to deliver a maximum throughput of 160 Mbps per sector (@ 80 MHz per sector). The information energy efficiency of the specific 4G LTE base station (e.g., W·s per bit) would be ca. 10 µJ/bit. The 4G LTE base station requires 10 micro (one millionth) Joules to deliver 1 bit (in 1 second).

In the 5G world, we would have a 5G SA base station, using the same frequency bands as 4G and with an additional 10 MHz @ 700MHz and 100 MHz @ 3.5 GHz included. The 3.5 GHz band is supported by an advanced antenna system (AAS) rather than a classical passive antenna system used for the other frequency bands. This configuration consumes 10 kW with ~40% attributed to the 3.5 GHz AAS, supporting ~1 Gbps per sector (@ 190 MHz per sector). This example’s 5G information energy efficiency would be ca. 0.3 µJ/bit.

In this non-apples-for-apples comparison, 5G is about 30 times more efficient in delivering a bit than 4G LTE (in the example above). Regarding what an operator actually pays for, 5G is twice as costly in energy consumption compared to 4G.

It should be noted that the power consumption is not driven by the volumetric demand but by the time that demand exists and the load per unit of time. Also, base stations will have a power consumption even when idle with the degree depending on the intelligence of the energy management system applied.

So, more formalistic, we have

E per bit = P (in W) · time (in sec) per bit, or in the basic units

J / bit = W·s / bit = W / (bit/s) = W / bps = W / [ MHz · Mbps/MHz/unit · unit-quantity ]

E per bit = P (in W) / [ Bandwidth (in MHz) · Spectral Efficiency (in Mbps/MHz/unit) · unit-quantity ]

It is important to remember that this is about the system spec information efficiency and that there is no direct relationship between the Power that you need and the outputted information your system will ultimately support bit-wise.

and

Thus, the relative efficiency between 4G and 5G is

Currently (i.e., 2023), the various components of the above are approximately within the following ranges.

The power consumption of a 5G RAT is higher than that of a 4G RAT. As we add higher frequency spectrum (e.g., C-band, 6GHz, 23GHz,…) to the 5G RAT, increasingly more spectral bandwidth (B) will be available compared to what was deployed for 4G. This will increase the bit-wise energy efficiency of 5G compared to 4G, although the power consumption is also expected to increase as higher frequencies are supported.

If the bandwidth and system power consumption is the same for both radio access technologies (RATs), then we have the relative information energy efficiency is

Depending on the relative difference in spectral efficiency. 5G is specified and designed to have at least ten times (10x) the spectral efficiency of 4G. If you do the math (assuming apples-to-apples applies), it is no surprise that 5G is specified to be 90% more efficient in delivering a bit (in a given unit of time) compared to 4G LTE.

And just to emphasize the obvious,

RAT refers to the radio access technology, BB is the baseband, freq the cellular frequencies, and idle to the situation where the system is not being utilized.

Volume in Bytes (or bits) does not directly relate to energy consumption. As frequency bands are added to a sector (of a base station), the overall power consumption will increase. Moreover, the more computing is required in the antenna, such as for advanced antenna systems, including massive MiMo antennas, the more power will be consumed in the base station. The more the frequency bands are being utilized in terms of time, the higher will the power consumption be.

Indirectly, as the cellular system is being used, customers consume bits and bytes (=8·bit) that will depend on the effective spectral efficiency (in bps/Hz), the amount of effective bandwidth (in Hz) experienced by the customers, e.g., many customers will be in a coverage situation where they may not benefit for example from higher frequency bands), and the effective time they make use of the cellular network resources. The observant reader will see that I like the term “effective.” The reason is that customers rarely enjoy the maximum possible spectral efficiency. Likely, not all the frequency spectrum covering customers is necessarily being applied to individual customers, depending on their coverage situation.

In the report “A Comparison of the Energy Consumption of Broadband Data Transfer Technologies (November 2021),” the authors show the energy and volumetric consumption of mobile networks in Finland over the period from 2010 to 2020. To be clear, I do not support the author’s assertion of causation between volumetric demand and energy consumption. As I have shown above, volumetric usage does not directly cause a given power consumption level. Over the 10-year period shown in the report, they observe a 70% increase in absolute power consumption (from 404 to 686 GWh, CAGR ~5.5%) and a factor of ~70 in traffic volume (~60 TB to ~4,000 TB, CAGR ~52%). Caution should be made in resisting the temptation to attribute the increase in energy over the period to be directly related to the data volume increase, however weak it is (i.e., note that the authors did not resist that temptation). Rudolf van der Berg has raised several issues with the approach of the above paper (as well as with many other related works) and indicated that the data and approach of the authors may not be reliable. Unfortunately, in this respect, it appears that systematic, reliable, and consistent data in the Telco industry is hard to come by (even if that data should be available to the individual telcos).

Technology change from 2G/3G to 4G, site densification, and more frequency bands can more than easily explain the increase in energy consumption (and all are far better explanations than data volume). It should be noted that there will also be reasons that decrease power consumption over time, such as more efficient electronics (e.g., via modernization), intelligent power management applications, and, last but not least, switching off of older radio access technologies.

The factors that drive a cell site’s absolute energy consumption is

  • Radio access technology with new technologies generally consumes more energy than older ones (even if the newer technologies have become increasingly more spectrally efficient).
  • The antenna type and configuration, including computing requirements for advanced signal processing and beamforming algorithms (that will improve the spectral efficiency at the expense of increased absolute energy consumption).
  • Equipment efficiency. In general, new generations of electronics and systems designs tend to be more energy-efficient for the same level of performance.
  • Intelligent energy management systems that allow for effective power management strategies will reduce energy consumption compared to what it would have been without such systems.
  • The network optimization goal policy. Is the cellular network planned and optimized for meeting the demands and needs of the customers (i.e., the economic design framework) or for providing the peak performance to as many customers as possible (i.e., the Umlaut/Ookla performance-driven framework)? The Umlaut/Ookla-optimized network, maxing out on base station configuration, will observe substantially higher energy consumption and associated costs.
The absolute cellular energy consumption has continued to rise as new radio access technologies (RAT) have been introduced irrespective of the leapfrog in those RATS spectral (bits per Hz) and information-related (Joules per bit) efficiencies.

WHY 5G IS NOT A GREEN TECHNOLOGY?

Let’s first re-acquaint ourselves with the 2015 vision of the 5G NGMN whitepaper;

“5G should support a 1,000 times traffic increase in the next ten years timeframe, with energy consumption by the whole network of only half that typically consumed by today’s networks. This leads to the requirement of an energy efficiency increase of x2000 in the next ten years timeframe.” (Section 4.2.2 Energy Efficiency, 5G White Paper by NGMN Alliance, February 2015).

The bold emphasis is my own and not in the paper itself. There is no doubt that the authors of the 5G vision paper had the ambition of making 5G a sustainable and greener cellular alternative than historically had been the case.

So, from the above statement, we have two performance figures that illustrate the ambition of 5G relative to 4G. Firstly, we have a requirement that the 5G energy efficiency should be 2000x higher than 4G (as it was back in the beginning of 2015).

or

if

Getting more spectrum bandwidth is relatively trivial as you go up in frequency and into, for example, the millimeter wave range (and beyond). However, getting 20+ GHz (e.g., 200+x100 MHz @ 4G) of additional practically usable spectrum bandwidth would be rather (=understatement) ambitious.

And that the absolute energy consumption of the whole 5G network should be half of what it was with 4G

If you think about this for a moment. Halfing the absolute energy consumption is an enormous challenge, even if it would have been with the same RAT. It requires innovation leapfrogs across the RAT electronic architecture, design, and material science underlying all of it. In other words, fundamental changes are required in the RF frontend (e.g., Power amplifiers, transceivers), baseband processing, DSP, DAC, ADC, cooling, control and management systems, algorithms, compute, etc…

But reality eats vision for breakfast … There really is no sign that the super-ambitious goal set by the NGMN Alliance in early 2015 is even remotely achievable even if we would give it another ten years (i.e., 2035). We are more than two orders of magnitude away from the visionary target of NGMN, and we are almost at the 10-year anniversary of the vision paper. We more or less get the benefit of the relative difference in spectral efficiency (x10), but no innovation beyond that has contributed very much to quantum leap cellular energy efficiency bit-wise.

I know many operators who will say that from a sustainability perspective, at least before the energy prices went through the roof, it really does not matter that 5G, in absolute terms, leads to substantial increases in energy consumption. They use green energy to supply the energy demand from 5G and pay off $CO_2$ deficits with certificates.

First of all, unless the increased cost can be recovered with the customers (e.g., price plan increase), it is a doubtful economic venue to pursue (and has a bit of a Titanic feel to it … going down together while the orchestra is playing).

Second, we should ask ourselves whether it is really okay for any industry to greedily consume sustainable and still relatively scarce green resources without being incentivized (or encouraged) to pursue alternatives and optimize across mobile and fixed broadband technologies. Particularly when fixed broadband technologies, such as fiber, are available, that would lead to a very sizable and substantial reduction in energy consumption … as customers increasingly adapt to fiber broadband.

Fiber is the greenest and most sustainable access technology we can deploy compared to cellular broadband technologies.

SO WHAT?

5G is a reality. Telcos are and will continue to invest substantially into 5G as they migrate their customers from 4G LTE to what ultimately will be 5G Standalone. The increase in customer experience and new capabilities or enablers are significant. By now, most Telcos will (i.e., 2023) have a very good idea of the operational expense associated with 5G (if not … you better do the math). Some will have been exploring investing in their own green power plants (e.g., solar, wind, hydrogen, etc.) to mitigate part of the energy surge arising from transitioning to 5G.

I suspect that as Telcos start reflecting on Open RAN as they pivot towards 6G (-> 2030+), above and beyond what 6G, as a RAT, may bring of additional operational expense pain, there will be new energy consumption and sustainability surprises to the cellular part of Telcos P&L. In general, breaking up an electronic system into individual (non-integrated) parts, as opposed to being integrated into a single unit, is likely to result in an increased power consumption. Some of the operational in-efficiencies that occur in breaking up a tightly integrated design can be mitigated by power management strategies. Though in order to get such power management strategies to work at the optimum may force a higher degree of supplier uniformity than the original intent of breaking up the tightly integrated system.

However, only Telcos that consider both their mobile and fixed broadband assets together, rather than two silos apart, will gain in value for customers and shareholders. Fixed-mobile (network) conversion should be taken seriously and may lead to very different considerations and strategies than 10+ years ago.

With increasing coverage of fiber and with Telcos stimulating aggressive uptake, it will allow those to redesign the mobile networks for what they were initially supposed to do … provide convenience and service where there is no fixed network present, such as when being mobile and in areas where the economics of a fixed broadband network makes it least likely to be available (e.g., rural areas) although LEO satellites (i.e., here today), maybe stratospheric drones (i.e., 2030+), may offer solid economic alternatives for those places. Interestingly, further simplifying the cellular networks supporting those areas today.

TAKE AWAY.

Volume in Bytes (or bits) does not directly relate to the energy consumption of the underlying communications networks that enable the usage.

The duration, the time scale, of the customer’s usage (i.e., the use of the network resources) does cause power consumption.

The bit-wise energy efficiency of 5G is superior to that of 4G LTE. It is designed that way via its spectral efficiency. Despite this, a 5G site configuration is likely to consume more energy than a 4G LTE site in the field and, thus, not a like-for-like in terms of number of bands and type of antennas deployed.

The absolute power consumption of a 5G configuration is a function of the number of bands deployed, the type of antennas deployed, intelligent energy management features, and the effective time 5G resources that customers have demanded.

Due to its optical foundation, Fiber is far more energy efficient in both bit-wise relative terms and absolute terms than any other legacy fixed (e.g., xDSL, HFC) or cellular broadband technology (e.g., 4G, 5G).

Looking forward and with the increasing challenges of remaining sustainable and contributing to CO2 reduction, it is paramount to consider an energy-optimized fixed and mobile converged network architecture as opposed to today’s approach of optimizing the fixed network separately from the cellular network. As a society, we should expect that the industry works hard to achieve an overall reduction in energy consumption, relaxing the demand on existing green energy infrastructures.

With 5G as of today, we are orders of magnitude from the original NGMN vision of energy consumption of only half of what was consumed by cellular networks ten years ago (i.e., 2014), requiring an overall energy efficiency increase of x2000.

Be aware that many Telcos and Infrastructure providers will use bit-wise energy efficiency when they report on energy consumption. They will generally report impressive gains over time in the energy that networks consume to deliver bits to their customers. This is the least one should expect.

Last but not least, the telco world is not static and is RAT-wise not very clean, as mobile networks will have several RATs deployed simultaneously (e.g., 2G, 4G, and 5G). As such, we rarely (if ever) have apples-to-apples comparisons on cellular energy consumption.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this article. I also greatly appreciate the discussion on this topic that I have had with Rudolf van der Berg over the last couple of years. I thank him for pointing out and reminding me (when I forget) of the shortfalls and poor quality of most of the academic work and lobbying activities done in this area.

PS

If you are aiming at a leapfrog in absolute energy reduction of your cellular network, above and beyond what you get with your infrastructure suppliers (e.g., Nokia, Ericsson, Huawei…), I really recommend you take a look at Opanga‘s machine learning-based Joule ML solution. The Joules ML has been proven to reduce RAN energy costs by 20% – 40% on top of what the RAT supplier’s (e.g., Ericsson, Nokia, Huawei, etc.) own energy management solutions may bring.

Disclosure: I am associated with Opanga and on their Industry Advisory Board.

On Cellular Data Pricing, Revenue & Consumptive Growth Dynamics, and Elephants in the Data Pipe.

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I am getting a bit sentimental as I haven’t written much about cellular data consumption for the last 10+ years. At the time, it did not take long for most folks in and out of our industry to realize that data traffic and, thereby, so many believed, the total cost of providing the cellular data would be growing far beyond the associated data revenues, e.g., remember the famous scissor chart back in the early two thousand tens. Many believed (then) that cellular data growth would be the undoing of the cellular industry. In 2011 many believed that the Industry only had a few more years before the total cost of providing cellular data would exceed the revenue rendering cellular data unprofitable. Ten years after, our industry remains alive and kicking (though they might not want to admit it too loudly).

Much of the past fear was due to not completely understanding the technology drivers, e.g., bits per second is a driver, and bytes that price plans were structured around not so much. The initial huge growth rates of data consumption that were observed did not make the unease smaller, i.e., often forgetting that a bit more can be represented as a huge growth rate when you start with almost nothing. Moreover, we also did have big scaling challenges with 3G data delivery. It became quickly clear that 3G was not what it had been hyped to be by the industry.

And … despite the historical evidence to the contrary, there are still to this day many industry insiders that believe that a Byte lost or gained is directly related to a loss or gain in revenue in a linear fashion. Our brains prefer straight lines and linear thinking, happily ignoring the unpleasantries of the non-linear world around us, often created by ourselves.

Figure 1 illustrates linear or straight-line thinking (left side), preferred by our human brains, contrasting the often non-linear reality (right side). It should be emphasized that horizontal and vertical lines, although linear, are not typically something that instinctively enters the cognitive process of assessing real-world trends.

Of course, if the non-linear price plans for cellular data were as depicted above in Figure 1, such insiders would be right even if anchored in linear thinking (i.e., even in the non-linear example to the right, an increase in consumption (GBs) leads to an increase in revenue). However, when it comes to cellular data price plans, the price vs. consumption is much more “beastly,” as shown below (in Figure 2);

Figure 2 illustrates the two most common price plan structures in Telcoland; (a, left side) the typical step function price logic that associates a range of data consumption with a price point, i.e., the price is a constant independent of the consumption over the data range. The price level is presented as price versus the maximum allowed consumption. This is by far the most common price plan logic in use. (b, right side) The “unlimited” price plan logic has one price level and allows for unlimited data consumption. T-Mobile US, Swisscom, and SK Telecom have all endorsed the unlimited with good examples of such pricing logic. The interesting fact is that most of those operators have several levels of unlimited tied to the consumptive behavior where above a given limit, the customer may be throttled (i.e., the speed will be reduced compared to before reaching the limit), or (and!) the unlimited plan is tied to either radio access technology (e.g., 4G, 4G+5G, 5G) or a given speed (e.g., 50 Mbps, 100 Mbps, 1Gbps, ..).

Most cellular data price plans follow a step function-like pricing logic as shown in Figure 2 (left side), where within each level, the price is constant up to the nominal data consumption value (i.e., purple dot) of the given plan, irrespective of the consumption. The most extreme version of this logic is the unlimited price plan, where the price level is independent of the volumetric data consumption. Although, “funny” enough, many operators have designed unlimited price plans that, in one way or another, depend on the customers’ consumption, e.g., after a certain level of unlimited consumption (e.g., 200 GB), cellular speed is throttled substantially (at least if the cell under which the customer demand resources are congested). So the “logic” is that if you wanted true unlimited, you still need to pay more than if you only require “unlimited”. Note that for the mathematically inclined, the step function is regarded as (piece-wise) linear … Although our linear brains might not appreciate that finesse very much. Maybe a heuristic that “The brain thinks in straight lines” would be more precisely restated as “The brain thinks in continuous non-constant monotonous straight lines”.

Any increase in consumption within a given pricing-consumption level will not result in any additional revenue. Most price plans allow for considerable growth without incurring additional associated revenues.

NETHERLANDS vs INDONESIA – BRIEFLY.

I like to keep informed and updated about markets I have worked in, with operators I have worked for, and with. I have worked across the globe in many very diverse markets and with operators in vastly different business cycles gives an interesting perspective on our industry. Throughout my career, I have been super interested in the difference between Telco operations and strategies in so-called mature markets versus what today may be much more of a misnomer than 10+ years ago, emerging markets.

The average cellular, without WiFi, consumption per customer in Indonesia was ca. 8 GB per month in 2022. That consumption would cost around 50 thousand Rp (ca. 3 euros) per month. For comparison, in The Netherlands, that consumption profile would cost a consumer around 16 euros per month. As of May 2023, the median cellular download speed was 106 Mbps (i.e., helped by countrywide 5G deployment, for 4G only, the speed would be around 60 to 80 Mbps) compared with 22 Mbps in Indonesia (i.e., where 5G has just been launched. Interestingly, although most likely coincidental, in Indonesia, a cellular data customer would pay ca. 5 times less than in the Netherlands for the same volumetric consumption. Note that for 2023, the average annual income in Indonesia is about one-quarter of that in the Netherlands. However, the Indonesian cellular consumer would also have one-fifth of the quality measured by downlink speed from the cellular base station to the consumer’s smartphone.

Let’s go deeper into how effective consumptive growth of cellular data is monetized… what may impact the consumptive growth, positively and negatively, and how it relates to the telco’s topline.

CELLULAR BUSINESS DYNAMICS.

Figure 3 Between 2016 and 2021, Western European Telcos lost almost 7% of their total cellular turnover (ca. 7+ billion euros over the markets I follow). This corresponds to a total revenue loss of ca. 1.4% per year over the period. To no surprise, the loss of cellular voice-based revenue has been truly horrendous, with an annual loss ca. 30%, although the Covid year (2021 and 2022, for that matter) was good to voice revenues (as we found ourselves confined to our homes and a call away from our colleagues). On the positive side, cellular data-based revenues have “positively” contributed to the revenue in Western Europe over the period (we don’t really know the counterfactual), with an annual growth of ca. 4%. Since 2016 cellular data revenues have exceeded that of cellular voice revenues and are 2022 expected to be around 70% of the total cellular revenue (for Western Europe). Cellular revenues have been and remain under pressure, even with a positive contribution from cellular data. The growth of cellular data volume (not including the contribution generated from WiFi usage) has continued to grow with a 38% annualized growth rate and is today (i.e., 2023) more than five times that of 2016. The annual growth rate of cellular data consumption per customer is somewhat lower ranging from the mid-twenties to the end-thirties percent. Needless to say that the corresponding cellular ARPU has not experienced anywhere near similar growth. In fact, cellular ARPU has generally been lowered over the period.

Some, in my opinion, obvious observations that are worth making on cellular data (I come to realize that although I find these obvious, I am often confronted with a lack of awareness or understanding of those);

Cellular data consumption grows much (much) faster than the corresponding data revenue (i.e., 38% vs 4% for Western Europe).

The unit growth of cellular data consumption does not lead to the same unit growth in the corresponding cellular data revenues.

Within most finite cellular data plans (thus the not unlimited ones), substantial data growth potential can be realized without resulting in a net increase of data-related revenues. This is, of course, trivial for unlimited plans.

The anticipated death of the cellular industry back in the twenty-tens was an exaggeration. The Industry’s death by signaling, voluptuous & unconstrained volumes of demanded data, and ever-decreasing euros per Bytes remains a fading memory and, of course, in PowerPoints of that time (I have provided some of my own from that period below). A good scare does wonders to stimulate innovation to avoid “Armageddon.” The telecom industry remains alive and well.

Figure 4 The latest data (up to 2022) from OECD on mobile data consumption dynamics. Source data can be found at OECD Data Explorer. The data illustrates the slowdown in cellular data growth from a customer perspective and in terms of total generated mobile data. Looking over the period, the 5-year cumulative growth rate between 2016 and 2021 is higher than 2017 to 2022 as well as the growth rate between 2022 and 2021 was, in general, even lower. This indicates a general slowdown in mobile data consumption as 4G consumption (in Western Europe) saturates and 5G consumption still picks up. Although this is not an account of the observed growth dynamics over the years, given the data for 2022 was just released, I felt it was worth including these for completeness. Unfortunately, I have not yet acquired the cellular revenue structure (e.g., voice and data) for 2022, it is work in progress.

WHAT DRIVES CONSUMPTIVE DATA GROWTH … POSITIVE & NEGATIVE.

What drives the consumer’s cellular data consumption? As I have done with my team for many years, a cellular operator with data analytics capabilities can easily check the list of positive and negative contributors driving cellular data consumption below.

Positive Growth Contributors:

  • Customer or adopter uptake. That is, new or old, customers that go from non-data to data customers (i.e., adopting cellular data).
  • Increased data consumption (i.e., usage per adopter) within the cellular data customer base that is driven by a lot of the enablers below;
  • Affordable pricing and suitable price plans.
  • More capable Radio Access Technology (RAT), e.g., HSDPA → HSPA+ → LTE → 5G, effectively higher spectral efficiency from advanced antenna systems. Typically will drive up the per-customer data consumption to the extent that pricing is not a barrier to usage.
  • More available cellular frequency spectrum is provisioned on the best RAT (regarding spectral efficiency).
  • Good enough cellular network consistent with customer demand.
  • Affordable and capable device ecosystem.
  • Faster mobile device CPU leads to higher consumption.
  • Faster & more capable mobile GPUs lead to higher consumption.
  • Device screen size. The larger the screen, the higher the consumption.
  • Access to popular content and social media.

Figure 5 illustrates the description of data growth as depending on the uptake of Adopters and the associated growth rate α(t) multiplied by the Usage per Adopter and the associated growth rate of usage μ(t). The growth of the Adopters can typically be approximated by an S-curve reaching its maximum as there are few more customers left to adopt a new service or product or RAT (i.e., α(t)→0%). As described in this section, the growth of usage per adopter, μ(t), will depend on many factors. Our intuition of μ is that it is positive for cellular data and historically has exceeded 30%. A negative μ would be an indication of consumptive churn. It should not be surprising that overall cellular data consumption growth can be very large as the Adopter growth rate is at its peak (i.e., around the S-curve inflection point), and Usage growth is high as well. It also should not be too surprising that after Adopter uptake has reached the inflection point, the overall growth will slow down and eventually be driven by the Usage per Adopter growth rate.

Figure 6 Using the OECD data (OECD Data Explorer) for the Western European mobile data per customer consumptive growth from 2011 to 2022, the above illustrates the annual growth rate of per-customer data mobile consumption. Mobile data consumption is a blend of usage across the various RATs enabling packet data usage. There is a clear increased annual growth after introducing LTE (4G) followed by a slowdown in annual growth, possibly due to reaching saturation in 4G adaptation, i.e., α3G→4G(t) → 0% leaving μ4G(t) driving the cellular data growth. There is a relatively weak increase in 2021, and although the timing coincides with 5G non-standalone (NSA) introduction (typically at 700 MHz or dynamics spectrum share (DSS) with 4G, e.g., Vodafone-Ziggo NL using their 1800 MHz for 4G and 5G) the increase in 2020 may be better attributed to Covid lockdown than a spurt in data consumption due to 5G NSA intro.

Anything that creates more capacity and quality (e.g., increased spectral efficiency, more spectrum, new, more capable RAT, better antennas, …) will, in general, result in an increased usage overall as well as on a per-customer basis (remember most price plans allow for substantial growth within the plans data-volume limit without incurring more cost for the customer). If one takes the above counterfactual, it should not be surprising that this would result in slower or negative consumption growth.

Negative growth contributors:

  • Cellular congestion causes increased packet loss, retransmissions, and deteriorating latency and speed performance. All in all, congestion may have a substantial negative impact on the customer’s service experience.
  • Throttling policies will always lower consumption and usage in general, as quality is intentionally lowered by the Telco.
  • Increased share of QUIC content on the network. The QUIC protocol is used by many streaming video providers (e.g., Youtube, Facebook, TikTok, …). The protocol improves performance (e.g., speed, latency, packet delivery, network changes, …) and security. Services using QUIC will “bully” other applications that use TCP/IP, encouraging TCP/IP to back off from using bandwidth. In this respect, QUIC is not a fair protocol.
  • Elephant flow dynamics (e.g., few traffic flows causing cell congestion and service degradation for the many). In general, elephant flows, particularly QUIC based, will cause an increase in TCP/IP data packet retransmissions and timing penalties. It is very much a situation where a few traffic flows cause significant service degradation for many customers.

One of the manifestations of cell congestion is packet loss and packet retransmission. Packet loss due to congestion ranges from 1% to 5%. or even several times higher at moments of peak traffic or if the user is in a poor cellular coverage area. The higher the packet loss, the worse the congestion, and the worse the customer experience. The underlying IP protocols will attempt to recover a lost packet by retransmission. The retransmission rate can easily exceed 10% to 15% in case of congestion. Generally, for a reliable and well-operated network, the packet loss should be well below 1% and even as low as 0.1%. Likewise, one would expect a packet retransmission rate of less than 2% (I believe the target should be less than 1%).

Thus, customers that happen to be under a given congested cell (e.g., caused by an elephant flow) would incur a substantially higher rate of retransmitted data packages (i.e., 10% to 15% or higher) as the TCP/IP protocol tries to make up for lost data packages. The customer may experience substantial service quality degradation and, as a final (unintended) “insult”, often be charged for those additional retransmitted data volumes.

From a cellular perspective, as the congestion has been relieved, the cellular operator may observe that the volume on the congested cell actually drops. The reason is that the packet loss and retransmission drops to a level far below the congested one (e.g., typically below 1%). As the quality improves for all customers demanding service from the previously overloaded (i.e., congested) cell, sustainable volume growth will commence in total and as well as will the average consumption on a customer basis. As will be shown below for normal cellular data consumption and most (if not all) price plans, a few percentage points drop in data volume will not have any meaningful effect on revenues. Either because the (temporary) drop happens within the boundaries of a given price plan level and thus has no effect on revenue, or because the overall gainful consumptive growth, as opposed to data volume attributed to poor quality, far exceeds the volume loss due to improved capacity and quality of a congested cell.

Well-balanced and available cellular sites will experience positive and sustainable data traffic growth.

Congested and over-loaded cellular sites will experience a negative and persistent reduction of data traffic.

Actively managing the few elephant flows and their negative impact on the many will increase customer satisfaction, reduce consumptive churn, and increase data growth, easily compensating for the congestion-induced increases due to packet retransmission. And unless an operator consistently is starved for radio access investments, or has poor radio access capacity management processes, most cell congestion can be attributed to the so-called elephant flows.

CELLULAR DATA CONSUMPTION IN REAL NETWORKS – ON A SECTOR LEVEL.

And irrespective of whatever drives positive and negative growth, it is worth remembering that daily traffic variations on a sector-by-sector basis and an overall cellular network level are entirely natural. An illustration of such natural sector variation over a (non-holiday) week is shown below in Figure 7 (c) for a sector in the top-20% of busiest sectors. In this example, the median variation over all sectors in the same week, as shown below, was around 10%. I often observe that even telco people (that should know better) find this natural variation quite worrisome as it appears counterintuitive to their linear growth expectations. Proper statistical measurement & analysis methodologies must be in place if inferences and solid analysis are required on a sector (or cell) basis over a relatively short time period (e.g., day, days, week, weeks,…).

Figure 7 illustrates the cellular data consumption daily variation over a (non-holiday) week. In the above, there are three examples (a) a sector from the bottom 20% in terms of carried volume, (b) a sector with a median data volume, and (c) a sector taken from the top 20% of carried data volume. Over the three different sectors (low, median, high) we observe very different variations over weekdays. From the top-20%, we have an almost 30% variation between the weekly minimum (Tuesday) and the weekly maximum (Thursday) to the bottom-20% with a variation in excess of 200% over the week. The charts above show another trend we observe in cellular networks regarding consumptive variations over time. Busy sectors tend to have a lower weekly variation than less busy sectors. I should point out that I have made no effort to select particular sectors. I could easily find some (of the less busy sectors) with even more wild variations than shown above.

The day-to-day variation is naturally occurring based on the dynamic behavior of the customers served by a given sector or cell (in a sector). I am frequently confronted with technology colleagues (whom I respect for their deep technical knowledge) that appear to expect (data) traffic on all levels monotonously increase with a daily growth rate that amounts to the annual CAGR observed by comparing the end-of-period volume level with the beginning of period volume level. Most have not bothered to look at actual network data and do not understand (or, to put it more nicely, simply ignore) the naturally statistical behavior of traffic that drives hourly, daily, weekly, and monthly variations. If you let statistical variations that you have no control over drive your planning & optimization decisions. In that case, you will likely fail to decide on the business-critical ones you can control.

An example of a high-traffic (top-20%) sector’s complete 365 day variations of data consumption is shown below in Figure 8. We observe that the average consumption (or traffic demand) increases nicely over the year with a bit of a slowdown (in this European example) during the summer vacation season (same around official holidays in general). Seasonal variations is naturally occurring and often will result in a lower-than-usual daily growth rate and a change in daily variations. In the sector traffic example below, Tuesdays and Saturdays are (typically) lower than the average, and Thursdays are higher than average. The annual growth is positive despite the consumptive lows over the year, which would typically freak out my previously mentioned industry colleagues. Of course, every site, sector, and cell will have a different yearly growth rate, most likely close to a normal distribution around the gross annual growth rate.

Figure 8 illustrates a top-20% sector’s data traffic growth dynamics (in GB) over a calendar year’s 365 days. Tuesdays and Saturdays are likely below the weekly average data consumption, and Thursdays are more likely to be above. Furthermore, the daily traffic growth is slowing around national holidays and in the summer vacation (i.e., July & August for this particular Western European country).

And to nail down the message. As shown in the example in Figure 9 below, every sector in your cellular network from one time period to the other will have a different positive and negative growth rate. The net effect over time (in terms of months more than days or weeks) is positive as long as customers adopt the supplied RAT (i.e., if customers are migrating from 4G to 5G, it may very well be that 4G consumed data will decline while the 5G consumed data will increase) and of course, as long as the provided quality is consistent with the expected and demanded quality, i.e., sectors with congestion, particular so-called elephant-flow induced congestion, will hurt the quality of the many that may reduce their consumptive behavior and eventually churn.

Figure 9 illustrates the variation in growth rates across 15+ thousand sectors in a cellular network comparing the demanded data volume between two consecutive Mondays per sector. Statistical analysis of the above data shows that the overall average value is ca. 0.49% and slightly skewed towards the positive growths rates (e.g., if you would compare a Monday with a Tuesday, the histogram would typically be skewed towards the negative side of the growth rates as Tuesday are a lower traffic day compared to Monday). Also, with the danger of pointing out the obvious, the daily or weekly growth rates expected from an annual growth rate of, for example, 30% are relatively minute, with ca. 0.07% and 0.49%, respectively.

The examples above (Figures 7, 8, and 9) are from a year in the past when Verstappen had yet to win his first F1 championship. That particular weekend also did not show F1 (or Sunday would have looked very different … i.e., much higher) or any other big sports event.

CELLULAR DATA PRICE PLAN LOGIC.

Figure 10 above is an example of the structure of a price plan. Possibly represented slightly differently from how your marketeer would do (and I am at peace with that). We observe the illustration of a price level of 8 data volume intervals on the upper left chart. This we can also write as (following the terminology of the lower right corner);

Thus, for the package allowing the customer to consume up to 3 GB is priced at 20 (irrespective of whether the customer would consume less). For package a consumer would pay 100 for a data consumption allowance up to 35 GB. Of course, we assume that the consumer choosing this package would generally consume more than 24 GB, which is the next cheaper package (i.e., ).

The price plan example above clearly shows that each price level offers customers room to grow before upgrading to the next level. For example, a customer consuming no more than 8 GB per month, fitting into , could increase consumption with 4 GB (+50%) before considering the next level price plan (i.e., ). This is just to illustrate that even if the customer’s consumption may grow substantially, one should not per se be expecting more revenue.

Even though it should be reasonably straightforward that substantial growth of a customer base data consumption cannot be expected to lead to an equivalent growth in revenue, many telco insiders instinctively believe this should be the case. I believe that the error may be due to many mentally linearizing the step-function price plans (see Figure 2 upper right side) and simply (but erroneously) believing that any increase (decrease) in consumption directly results in an increase (or decrease) in revenue.

DATA PRICING LOGIC & USAGE DISTRIBUTION.

If we want to understand how consumptive behavior impacts cellular operators’ toplines, we need to know how the actual consumption distributes across the pricing logic. As a high-level illustration, Figure 11 (below) shows the data price step-function logic from Figure 9 with an overall consumptive distribution superimposed (orange solid line). It should be appreciated that while this provides a fairly clear way of associating consumption with pricing, it is an oversimplification at best. It will nevertheless allow me to estimate crudely the number of customers that are likely to have chosen a particular price plan matching their demand (and affordability). In reality, we will have customers that have chosen a given price plan but either consume less than the limit of the next cheaper plan (thus, if consistently so, could save but go to that plan). We will also have customers that consume more than their allowed limit. Usually, this would result in the operator throttling the speed and sending a message to the customer that the consumption exceeds the limit of the chosen price plan. If a customer would consistently overshoots the limits (with a given margin) of the chosen plan, it is likely that eventually, the customer will upgrade to the next more expensive plan with a higher data allowance.

Figure 11 above illustrates on the left side a consumptive distribution (orange line) identified by its mean and standard deviation superimposed on our price plan step-function logic example. The right summarizes the consumptive distribution across the eight price plan levels. Note that there is a 9th level in case the 200 GB limit is breached (0.2% in this example). I am assuming that such customers pay twice the price for the 200 GB price plan (i.e., 320).

In the example of an operator with 100 million cellular customers, the consumptive distribution and the given price plan lead to a fiat of 7+ billion per month. However, with a consumptive growth rate of 30% to 40% annually per active cellular data user (on average), what kind of growth should we expect from the associated cellular data revenues?

Figure 12 In the above illustration, I have mapped the consumptive distribution to the price plan levels and then developed the begin-of-period consumptive distribution (i.e., the light green curve) month by month until month 12 has been reached (i.e., the yellow curve). I assume the average monthly consumptive cellular data growth is 2.5% or ca. 35% after 12 months. Furthermore, I assume that for the few customers falling outside the 200 GB limit that they will purchase another 200 GB plan. For completeness, the previous 12 months (previous year) need to be carried out to compare the total cumulated cellular data revenue between the current and previous periods.

Within the current period (shown in Figure 12 above), the monthly cellular data revenue CAGR comes out at 0.6% or a total growth of 7.4% of monthly revenue between the beginning period and the end period. Over the same period, the average data consumption (per user) grew by ca. 34.5%. In terms of the current year’s total data revenue to the previous year’s total data revenue, we get an annual growth rate of 8.3%. This illustrates that it should not be surprising that the revenue growth can be far smaller than the consumptive growth given price plans such as the above.

It should be pointed out that the above illustration of consumptive and revenue growth simplifies the growth dynamics. For example, the simulation ignores seasonal swings over a 12-month period. Also, it attributes 1-to-1 all consumption falling within the price range to that particular price level when there is always spillover on both upper and lower levels of a price range that will not incur higher or lower revenues. Moreover, while mapping the consumptive distribution to the price-plan giga-byte intervals makes the simulation faster (and setup certainly easier), it is also not a very accurate approach to the coarseness of the intervals.

A LEVEL DEEPER.

While working with just one consumptive distribution, as in Figure 11 and Figure 12 above, allows for simpler considerations, it does not fully reflect the reality that every price plan level will have its own consumptive distribution. So let us go that level deeper and see whether it makes a difference.

Figure 13 above, illustrates the consumptive distribution within a given price plan range, e.g., the “5 GB @ 30” price-plan level for customers with a consumption higher than 3 GB and less than or equal to 5 GB. It should come as no surprise that some customers may not reach even the 3 GB, even though they pay for (up to) 5 GB, and some may occasionally exceed the 5 GB limit. In the example above, 10% of customers have a consumption below 3 GB (and could have chosen the next cheaper plan of up to 3 GB), and 3% exceed the limits of the chosen plan (an event that may result in the usage speed being throttled). As the average usage within a given price plan level approaches the ceiling (e.g., 5 GB in the above illustration), in general, the standard deviation will reduce accordingly as customers will jump to the Next Expensive Plan to meet their consumptive needs (e.g., “12 GB @ 50” level in the illustration above).

Figure 14 generalizes Figure 11 to the full price plan and, as illustrated in Figure 12, let the consumption profiles develop in time over a 12-month period (Initial and +12 month shown in the above illustration). The difference between the initial and 12 months can be best appreciated with the four smaller figures that break up the price plan levels in 0 to 40 GB and 40 to 200 GB.

The result in terms of cellular data revenue growth is comparable to that of the higher-level approach of Figure 12 (ca. 8% annual revenue growth vs 34 % overall consumptive annual growth rate). The detailed approach of Figure 11 is, however, more complicated to get working and requires much more real data to work with (which obviously should be available to operators in this time and age). One should note that in the illustrated example price plan (used in the figures above) that at a 2.5% monthly consumptive growth rate (i.e., 34% annually), it would take a customer an average of 24 months (spread of 14 to 35 month depending on level) to traverse a price plan level from the beginning of the level (e.g., 5 GB) to the end of the level (12 GB). It should also be clear that as a customer enters the highest price plan levels (e.g., 100 GB and 200 GB), little additional can be expected to be earned on those customers over their consumptive lifetime.

The illustrated detailed approach shown above is, in particular, useful to test a given price plan’s profitability and growth potential, given the particularities of the customers’ consumptive growth dynamics.

The additional finesse that could be considered in the analysis could be an affordability approach because the growth within a given price level slows down as the average consumption approaches the limit of the given price level. This could be considered by slowing the mean growth rate and allowing for the variance to narrow as the density function approaches the limit. In my simpler approach, the consumptive distributions will continue to grow at a constant growth rate. In particular, one should consider more sophisticated approaches to modeling the variance that determines the spillover into less and more expensive levels. An operator should note that consumption that reduces or consistently falls into the less expensive level expresses consumptive churn. This should be monitored on a customer level as well as on a radio access cell level. Consumptive churn often reflects the supplied radio access quality is out of sync with the customer demand dynamics and expectations. On a radio access cell level, the diligent operator will observe a sharp increase in retransmitted data packages and increased latency on a flow (and active customer basis) hallmarks of a congested cell.

WRAPPING UP.

To this day, 20+ odd years after the first packet data cellular price plans were introduced, I still have meetings with industry colleagues where they state that they cannot implement quality-enhancing technologies for the fear that data consumption may reduce and by that their revenues. Funny enough, often the fear is that by improving the quality for typically many of their customers being penalized by a few customers’ usage patterns (e.g., the elephants in the data pipe), the data packet loss and TCP/IP retransmissions are reducing as the quality is improving and more customers are getting the service they have paid for. It is ignoring the commonly established fact of our industry that improving the customer experience leads to sustainable growth in consumption that consequently may also have a positive topline impact.

I am often in situations where I am surprised with how little understanding and feeling Telco employees have for their own price plans, consumptive behavior, and the impact these have on their company’s performance. This may be due to the fairly complex price plans telcos are inventing, and our brain’s propensity for linear thinking certainly doesn’t make it easier. It may also be because Telcos rarely spend any effort educating their employees about their price plans and products (after all, employees often get all the goodies for “free”, so why bother?). Do a simple test at your next town hall meeting and ask your CXOs about your company’s price plans and their effectiveness in monetizing consumption.

So what to look out for?

Many in our industry have an inflated idea (to a fault) about how effective consumptive growth is being monetized within their company’s price plans.

Most of today’s cellular data plans can accommodate substantial growth without leading to equivalent associated data revenue growth.

The apparent disconnect between the growth rate of cellular data consumption (CAGR ~30+%), in its totality as well on an average per-customer basis, and cellular data revenues growth rate (CAGR < 10%) is simply due to the industry’s price plan structures allowing for substantial growth without a proportion revenue growth.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog.

FURTHER READING.

Kim Kyllesbech Larsen, Mind Share: Right Pricing LTE … and Mobile Broadband in general (A Technologist’s observations) (slideshare.net), (May 2012). A cool seminal presentation on various approaches to pricing mobile data. Contains a lot of data that illustrates how far we have come over the last 10 years.

Kim Kyllesbech Larsen, Mobile Data-centric Price Plans – An illustration of the De-composed. | techneconomyblog (February, 2015). Exploring UK mobile mixed-services price plans in an attempt to decipher the price of data which at the time (often still is) a challenge to figure out due to (intentional?) obfuscation.

Kim Kyllesbech Larsen, The Unbearable Lightness of Mobile Voice. | techneconomyblog (January, 2015). On the demise of voice revenue and rise of data. More of a historical account today.

Tellabs “End of Profit” study executive summary (wordpress.com), (2011). This study very much echoed the increasing Industry concern back in 2010-2012 that cellular data growth would become unprofitable and the industry’s undoing. The basic premise was that the explosive growth of cellular data and, thus, the total cost of maintaining the demand would lead to a situation where the total cost per GB would exceed the revenue per GB within the next couple of years. This btw. was also a trigger point for many cellular-focused telcos to re-think their strategies towards the integrated telco having internal access to fixed and mobile broadband.

B. de Langhe et al., “Linear Thinking in a Nonlinear World”, Harvard Business Review, (May-June, 2017). It is a very nice and compelling article about how difficult it is to get around linear thinking in a non-linear world. Our brains prefer straight lines and linear patterns and dependencies. However, this may lead to rather amazing mistakes and miscalculations in our clearly nonlinear world.

OECD Data Explorer A great source of telecom data, for example, cellular data usage per customer, and the number of cellular data customers, across many countries. Recently includes 2022 data.

I have used Mobile Data – Europe | Statista Market Forecast to better understand the distribution between cellular voice and data revenues. Most Telcos do not break out their cellular voice and data revenues from their total cellular revenues. Thus, in general, such splits are based on historical information where it was reported, extrapolations, estimates, or more comprehensive models.

Kim Kyllesbech Larsen, The Smartphone Challenge (a European perspective) (slideshare.net) (April 2011). I think it is sort of a good account for the fears of the twenty-tens in terms of signaling storms, smartphones (=iPhone) and unbounded traffic growth, etc… See also “Eurasia Mobile Markets Challenges to our Mobile Networks Business Model” (September 2011).

Geoff Huston, “Comparing TCP and QUIC”, APNIC, (November 2022).

Anna Saplitski et al., “CS244 ’16: QUIC loss recovery”, Reproducing Network Research, (May 2016).

RFC9000, “QUIC: A UDP-Based Multiplexed and Secure Transport“, Internet Engineering Task Force (IETF), (February 2022).

Dave Gibbons, What Are Elephant Flows And Why Are They Driving Up Mobile Network Costs? (forbes.com) (February 2019).

K.-C. Lan and J. Heidemann, “A measurement study of correlations of Internet flow characteristic” (February 2006). This seminal paper has inspired many other research works on elephant flows. A flow should be understood as an unidirectional series of IP packets with the same source and destination addresses, port numbers, and protocol numbers. The authors define elephant flows as flows with a size larger than the mean plus three standard deviations of the sampled data. Though it is important to point out that the definition is less important. Such elephant flows are typically few (less than 20%) but will cause cell congestion by reducing the quality of many requiring a service in such an affected cell.

Opanga Networks is a fascinating and truly innovative company. Using AI, they have developed their solution around the idea of how to manage data traffic flows, reduce congestion, and increase customer quality. Their (N2000) solution addresses particular network situations where a limited number of customer data usage takes up a disproportionate amount of resources within the cellular network (i.e., the problem with elephant flows). Opanga’s solution optimizes those traffic congestion-impacting flows and results in an overall increase in service quality and customer experience. Thus, the beauty of the solution is that the few traffic patterns, causing the cellular congestion, continue without degradation, allowing the many traffic patterns that were impacted by the few to continue at their optimum quality level. Overall, many more customers are happy with their service. The operator avoids an investment of relatively poor return and can either save the capital or channel it into a much higher IRR (internal rate of return) investment. I have seen tangible customer improvements exceeding 30+ percent improvement to congested cells, avoiding substantial RAN Capex and resulting Opex. And the beauty is that it does not involve third-party network vendors and can be up and running within weeks with an investment that is easily paid back within a few months. Opanga’s product pipeline is tailor-made to alleviate telecom’s biggest and thorniest challenges. Their latest product, with the appropriate name Joules, enables substantial radio access network energy savings above and beyond what features the telcos have installed from their Radio Access Network suppliers. Disclosure: I am associated with Opanga as an advisor to their industrial advisory board.

The Nature of Telecom Capex – a 2023 Update.

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CAPEX … IT’S PERSONAL

I built my first Telco technology Capex model back in 1999. I had just become responsible for what then was called Fixed Network Engineering with a portfolio of all technology engineering design & planning except for the radio access network but including all transport aspects from access up to Core and out to the external world. I got a bit frustrated that every time an assumption changed (e.g., business/marketing/sales), I needed to involve many people in my organization to revise their Capex demand. People that were supposed to get our greenfield network rolled out to our customers. Thus, I built my first Capex model that would take the critical business assumptions, size my network (including the radio access network), and consistently assign the right Capex amounts to each category. The model allowed for rapid turnaround on revised business assumptions and a highly auditable track of changes, planning drivers, and unit prices. Since then, I have built best-practice Capex (and technology Opex) models for many Deutsche Telekom AGs and Ooredoo Group entities. Moreover, I have been creating numerous network and business assessment and valuation models (with an eye on M&A), focusing on technology drivers behind Capex and Opex for many different types of telco companies (30+) operating in an extensive range of market environments around the world (20+). Creating and auditing techno-economical models, making those operational and of high quality, it has (for me) been essential to be extensively involved operationally in the telecom sector.

PRELUDE TO CAPEX.

Capital investments, or Capital Expenditures, or just Capex for short, make Telcos go around. Capex is the monetary means used by your Telco to acquire, develop, upgrade, modernize, and maintain tangible, as well as, in some instances, intangible, assets and infrastructure. We can find Capex back under “Property, Plants, and Buildings” (or PPB) in a company’s balance sheet or directly in the profit & loss (or income) statement. Typically for an investment to be characterized as a capital expense, it needs to have a useful lifetime of at least 2 years and be a physical or tangible asset.

What about software? A software development asset is, by definition, intangible or non-physical. However, it can, and often is, assigned Capex status, although such an assignment requires a bit more judgment (and auditorial approvals) than for a real physical asset.

The “Modern History of Telecom” (in Europe) is well represented by Figure 1, showing the fixed-mobile total telecom Capex-to-Revenue ratio from 1996 to 2025.

From 1996 to 2012, most of the European Telco Capex-to-Revenue ratio was driven by investment into mobile technology introductions such as 2G (GSM) in 1996 and 3G (UMTS) in 2000 to 2002 as well as initial 4G (LTE) investments. It is clear that investments into fixed infrastructure, particularly modernizing and enhancing, have been down-prioritized only until recently (e.g., up to 2010+) when incumbents felt obliged to commence investing in fiber infrastructure and urgent modernization of incumbents’ fixed infrastructures in general. For a long time, the investment focus in the telecom industry was mobile networks and sweating the fixed infrastructure assets with attractive margins.

Figure 1 illustrates the “Modern History of Telecom” in Europe. It shows the historical development of Western Europe Telecom Capex to Revenue ratio trend from 1996 to 2025. The maximum was about 28% at the time 2G (GSM) was launched and at minimum after the cash crunch after ultra-expensive 3G licenses and the dot.com crash of 2020. In recent years, since 2008, Capex to Revenue has been steadily increasing as 4G was introduced and fiber deployment started picking up after 20210. It should be emphasized that the Capex to Revenue trend is for both Mobile and Fixed. It does not include frequency spectrum investments.

Across this short modern history of telecom, possibly one of the worst industry (and technology) investments have been the investments we did into 3G. In Europe alone, we invested 100+ billion Euro (i.e., not included in the Figure) into 2100 MHz spectrum licenses that were supposed to provide mobile customers “internet-in-their-pockets”. Something that was really only enabled with the introduction of 4G from 2010 onwards.

Also, from 2010 onwards, telecom companies (in Europe) started to invest increasingly in fiber deployment as well as upgrading their ailing fixed transport and switching networks focusing on enabling competitive fixed broadband services. But fiber investments have picked up in a significant way in the overall telecom Capex, and I suspect it will remain so for the foreseeable future.

Figure 2 When we take the European Telco revenue (mobile & fixed) over the period 1996 to 2025, it is clear that the mobile business model quantum leaped revenue from its inception to around 2008. After this, it has been in steady decline, even if improvement has been observed in the fixed part of the telco business due to the transition from voice-dominated to broadband. Source: https://stats.oecd.org/

As can be observed from Figure 1, since the telecom credit crunch between 2000 and 2003, the Capex share of revenue has steadily increased from just around 12% in 2004, right after the credit crunch, to almost 20% in 2021. Over the period from 2008 to 2021, the industry’s total revenue has steadily declined, as can be seen in Figure 2. Taking the last 10 years (2011-2021) of mobile and fixed revenue data has, on average, reduced by 4+ billion euros a year. The cumulative annual growth rate (CAGR) was at a great +6% from the inception of 2G services in 1996 to 2008, the year of the “great recession.” From 2008 until 2021, the CAGR has been almost -2% in annual revenue loss for Western Europe.

What does that mean for the absolute total Capex spend over the same period? Figure 3 provides the trend of mobile and fixed Capex spending over the period. Since the “happy days” of 2G and 3G Capex spending, Capex rapidly declined after the industry spent 100+ billion Euro on 3G spectrum alone (i.e., 800+ million euros per MHz or 4+ euros per MHz-pop) before the required multi-billion Euro in 3G infrastructure. Though, after 2009, which was the lowest Capex spend after the 3G licenses were acquired, the telecom industry has steadily grown its annual total Capex spend with ca. +1 billion Euro per year (up to 2021) financing new technology introductions (4G and 5G), substantial mobile radio and core modernizations (a big refresh ca. every 6 -7 years), increasing capacity to continuously cope with consumer demand for broadband, fixed transport, and core infrastructure modernization, and last but not least (since the last ~ 8 years) increasing focus on fiber deployment. Over the same period from 2009 to 2021, the total revenue has declined by ca. 5 billion euros per year in Western Europe.

Figure 3 Using the above “Total Capex to Revenue” (Figure 1) and “Total Revenue” (Figure 2) allows us to estimate the absolute “Total Capex” over the same period. Apart from the big Capex swing around the introduction of 2G and 3G and the sharp drop during the “credit crunch” (2000 – 2003), Capex has grown steadily whilst the industry revenue has declined.

It will be very interesting to see how the next 10 years will develop for the telecom industry and its capital investment. There is still a lot to be done on 5G deployment. In fact, many Telcos are just getting started with what they would characterize as “real 5G”, which is 5G standalone at mid-band frequencies (e.g., > 3 GHz for Europe, 2.5 GHz for the USA), modernizing antenna structures from standard passive (low-order) to active antenna systems with higher-order MiMo antennas, possible mmWave deployments, and of course, quantum leap fiber deployment in laggard countries in Europe (e.g., Germany, UK, Greece, Netherlands, … ). Around 2028 to 2030, it would be surprising if the telecoms industry would not commence aggressively selling the consumer the next G. That is 6G.

At this moment, the next 3 to 5 years of Capital spending are being planned out with the aim of having the 2024 budgets approved by November or December. In principle, the long-term plans, that is, until 2027/2028, have agreed on general principles. Though, with the current financial recession brewing. Such plans would likely be scrutinized as well.

I have, over the last year since I published this article, been asked whether I had any data on Ebitda over the period for Western Europe. I have spent considerable time researching this, and the below chart provides my best shot at such a view for the Telecom industry in Western Europe from the early days of mobile until today. This, however, should be taken with much more caution than the above Caex and Revenues, as individual Telco’ s have changed substantially over the period both in their organizational structure and how results have been represented in their annual reports.

Figure 4 illustrates the historical development of the EBITDA margin over the period from 1995 to 2022 and a projection of the possible trends from 2023 onwards. Caution: telcos’ corporate and financial structures (including reporting and associated transparency into details) have substantially changed over the period. The early first 10+ years are more uncertain concerning margin than the later years. Directionally it is representative of the European Telco industry. Take Deutsche Telekom AG, it “lost” 25% of its revenue between 2005 and 2015 (considering only German & European segments). Over the same period, it shredded almost 27% of its Opex.

CAVEATS

Of course, Capex to Revenue ratios, any techno-economical ratio you may define, or cost distributions of any sort are in no way the whole story of a Telco life-and-budget cycle. Over time, due to possible structural changes in how Telcos operate, the past may not reflect the present and may even be less telling in the future.

Telcos may have merged with other Telcos (e.g., Mobile with Fixed), they may have non-Telco subsidiaries (i.e., IT consultancies, management consultancies, …), they may have integrated their fixed and mobile business units, they may have spun off their infrastructure, making use of towercos for their cell site needs (e.g., GD Towers, Vantage, Cellnex, American Towers …), open fibercos (e.g., Fiberhost Poland, Open Dutch Fiber, …) for their fiber needs, hyperscale cloud providers (e.g., AWS, Amazon, Microsoft Azure, ..) for their platform requirements. Capex and Opex will go left and right, up and down, depending on each of the above operational elements. All that may make comparing one Telco’s Capex with another Telco’s investment level and operational state-of-affairs somewhat uncertain.

I have dear colleagues who may be much more brutal. In general, they are not wrong but not as brutally right as their often high grounds could indicate. But then again, I am not a black-and-white guy … I like colors.

So, I believe that investment levels, or more generally, cost levels, can be meaningfully compared between Telcos. Cost, be it Opex or Capex, can be estimated or modeled with relatively high accuracy, assuming you are in the know. It can be compared with other comparables or non-comparables. Though not by your average financial controller with no technology knowledge and in-depth understanding.

Alas, with so many things in this world, you must understand what you are doing, including the limitations.

IT’S THAT TIME OF THE YEAR … CAPEX IS IN THE AIR.

It is the time of the year when many telcos are busy updating their business and financial planning for the following years. It is not uncommon to plan for 3 to 5 years ahead. It involves scenario planning and stress tests of those scenarios. Scenarios would include expectations of how the relevant market will evolve as well as the impact of the political and economic environment (e.g., covid lockdowns, the war in Ukraine, inflationary pressures, supply-chain challenges, … ) and possible changes to their asset ownership (e.g., infrastructure spin-offs).

Typically, between the end of the third or beginning of the fourth quarter, telecommunications businesses would have converged upon a plan for the coming years, and work will focus on in-depth budget planning for the year to come, thus 2024. This is important for the operational part of the business, as work orders and purchase orders for the first quarter of the following year would need to be issued within the current year.

The planning process can be sophisticated, involving many parts of the organization considering many scenarios, and being almost mathematical in its planning nature. It can be relatively simple with the business’s top-down financial targets to adhere to. In most instances, it’s likely a combination of both. Of course, if you are a publicly-traded company or part of one, your past planning will generally limit how much your new planning can change from the old. That is unless you improve upon your old plans or have no choice but to disappoint investors and shareholders (typically, though, one can always work on a good story). In general, businesses tend to be cautiously optimistic about uncertain business drivers (e.g., customer growth, churn, revenue, EBITDA) and conservatively pessimistic on business drivers of a more certain character (e.g., Capex, fixed cost, G&A expenses, people cost, etc..). All that without substantially and negatively changing plans too much between one planning horizon to the next.

Capital expense, Capex, is one of the foundations, or enablers, of the telco business. It finances the building, expansion, operation, and maintenance of the telco network, allowing customers to enjoy mobile services, fixed broadband services, TV services, etc., of ever-increasing quality and diversity. I like to look at Capex as the investments I need to incur in order to sustain my existing revenues, grow my revenues (preferably beating inflationary pressures), and finance any efficiency activities that will reduce my operational expenses in the future.

If we want to make the value of Capex to the corporation a little firmer, we need a little bit of financial calculus. We can write a company’s value (CV) as

With g being the expected growth rate in free cash flow in perpetuity, WACC is the Weighted Average Cost of Capital, and FCFF is the Free Cash Flow to the Firm (i.e., company) that we can write as follows;

FCFF = NOPLAT + Depreciation & Amortization (DA) – ∆ Working Capital – Capex,

with NOPLAT being the Net Operating Profit Less Adjusted Taxes (i.e., EBIT – Cash Taxes). So if I have two different Capex budgets with everything else staying the same despite the difference in Capex (if true life would be so easy, right?);

assuming that everything except the proposed Capex remains the same. With a difference of, for example, 10 Million euros, a future growth rate g = 0% (maybe conservative), and a WACC of 5% (note: you can find the latest average WACC data for the industry here, which is updated regularly by New York University Leonard N. Stern School of Business. The 5% chosen here serves as an illustration only (e.g., this was approximately representative of Telco Europe back in 2022, as of July 2023, it was slightly above 6%). You should always choose the weighted average cost of capital that is applicable to your context). The above formula would tell us that the investment plan having 10 Million euros less would be 200 Million euros more valuable (20× the Capex not spent). Anyone with a bit of (hands-on!) experience in budget business planning would know that the above valuation logic should be taken with a mountain of salt. If you have two Capex plans with no positive difference in business or financial value, you should choose the plan with less Capex (and don’t count yourself rich on what you did not do). Of course, some topics may require Capex without obvious benefits to the top or bottom line. Such examples are easy to find, e.g., regulatory requirements or geo-political risks force investments that may appear valueless or even value destructive. Those require meticulous considerations, and timing may often play a role in optimizing your investment strategy around such topics. In some cases, management will create a narrative around a corporate investment decision that fits an optimized valuation, typically hedging on one-sided inflated risks to the business if not done. Whatever decision is made, it is good to remember that Capex, and resulting Opex, is in most cases a certainty. The business benefits in terms of more revenue or more customers are uncertain as is assuming your business will be worth more in a number of years if your antennas are yellow and not green. One may call this the “Faith-based case of more Capex.”

Figure 5 provides an overview of Western Europe of annual Fixed & Mobile Capex, Total and Service Revenues, and Capex to Revenue ratio (in %). Source: New Street Research Western Europe data.

Figure 5 provides an overview of Western European telcos’ revenue, Capex, and Capex to Revenue ratio. Over the last five years, Western European telcos have been spending increasingly higher Capex levels. In 2021 the telecom Capex was 6 billion euros higher than what was spent in 2017, about 13% higher. Fixed and mobile service revenue increased by 14 billion euros, yielding a Capex to Service revenue ratio of 23% in 2021 compared to 20.6% in 2017. In most cases, the total revenue would be reported, and if luck has its way (or you are a subscriber to New Street Research), the total Capex. Thus, capturing both the mobile and the fixed business, including any non-service-related revenues from the company. As defined in this article, non-service-related revenues would comprise revenues from wholesales, sales of equipment (e.g., mobile devices, STB, and CPEs), and other non-service-specific revenues. As a rule of thumb, the relative difference between total and service-related revenues is usually between 1.1 to 1.3 (e.g., the last 5-year average for WEU was 1.17). 

One of the main drivers for the Western European Capex has firstly been aggressive fiber-to-the-premise (FTTP) deployment and household fiber connectivity, typically measured in homes passed across most of the European metropolitan footprint as well as urban areas in general. As fiber covers more and more residential households, increased subscription to fiber occurs as well. This also requires substantial additional Capex for a fixed broadband business. Figure 6 illustrates the annual FTTP (homes passed) deployment volume in Western Europe as well as the total household fiber coverage.

Figure 6 above shows the fiber to the premise (FTTP) home passed deployment per anno from 2018 to 2021 Actual (source: European Commission’s “Broadband Coverage in Europe 2021” authored by Omdia et al.) and 2021 to 2025 projected numbers (i.e., this author’s own assessment). During the period from 2018 to 2021, household fiber coverage grew from 27% to 43% and is expected to grow to at least 71% by 2026 (not including overbuilt, thus unique household covered). The overbuilt data are based on a work in progress model and really should be seen as directional (it is difficult to get data with respect to the overbuilt).

A large part of the initial deployment has been in relatively dense urban areas as well as relying on aerial fiber deployment outside bigger metropolitan centers. For example, in Portugal, with close to 90% of households covered with fiber as of 2021, the existing HFC infrastructure (duct, underground passageways, …) was a key enabler for the very fast, economical, and extensive household fiber coverage there. Although many Western European markets will be reaching or exceeding 80% of fiber coverage in their urban areas, I would expect to continue to see a substantial amount of Capex being attributed. In fact, what is often overlooked in the assessment of the Capex volume being committed to fiber deployment, is that the unit-Capex is likely to increase substantially as countries with no aerial deployment option pick up their fiber rollout pace (e.g., Germany, the UK, Netherlands) and countries with an already relatively high fiber coverage go increasingly suburban and rural.

Figure 7 above shows the total fiber to the premise (FTTP) home remaining per anno from 2018 to 2021 Actual (source: European Commission’s “Broadband Coverage in Europe 2021” authored by Omdia et al.). The 2022 to 2030 projected remaining households are based on the author’s own assessment and does not consider overbuilt numbers.

The second main driver is in the domain of mobile network investment. The 5G radio access deployment has been a major driver in 2020 and 2021. It is expected to continue to contribute significantly to mobile operators Capex in the coming 5 years. For most Western European operators, the initial 5G deployment was at 700 MHz, which provides a very good 5G coverage. However, due to limited frequency spectral bandwidth, there are not very impressive speeds unless combined with a solid pre-existing 4G network. The deployment of 5G at 700 MHz has had a fairly modest effect on Mobile Capex (apart from what operators had to pay out in the 5G spectrum auctions to acquire the spectrum in the first place). Some mobile networks would have been prepared to accommodate the 700 MHz spectrum being supported by existing lower-order or classical antenna infrastructure. In 2021 and going forward, we will see an increasing part of the mobile Capex being allocated to 3.X GHz deployment. Far more sophisticated antenna systems, which co-incidentally also are far more costly in unit-Capex terms, will be taken into use, such as higher-order MiMo antennas from 8×8 passive MiMo to 32×32 and 64×64 active antennas systems. These advanced antenna systems will be deployed widely in metropolitan and urban areas. Some operators may even deploy these costly but very-high performing antenna systems in suburban and rural clutter with the intention to provide fixed-wireless access services to areas that today and for the next 5 – 7 years continue to be under-served with respect to fixed broadband fiber services.

Overall, I would also expect mobile Capex to continue to increase above and beyond the pre-2020 level.

As an external investor with little detailed insights into individual telco operations, it can be difficult to assess whether individual businesses or the industry are investing sufficiently into their technical landscape to allow for growth and increased demand for quality. Most publicly available financial reporting does not provide (if at all) sufficient insights into how capital expenses are deployed or prioritized across the many facets of a telco’s technical infrastructure, platforms, and services. As many telcos provide mobile and fixed services based on owned or wholesaled mobile and fixed networks (or combinations there off), it has become even more challenging to ascertain the quality of individual telecom operations capital investments.

Figure 8 illustrates why analysts like to plot Total Revenue against Total Capex (for fixed and mobile). It provides an excellent correlation. Though great care should be taken not to assume causation is at work here, i.e., “if I invest X Euro more, I will have Y Euro more in revenues.” It may tell you that you need to invest a certain level of Capex in sustaining a certain level of Revenue in your market context (i.e., country geo-socio-economic context). Source: New Street Research Western Europe data covering the following countries: AT, BE, DK, FI, FR, DE, GR, IT, NL, NO, PT, ES, SE, CH, and UK.

Why bother with revenues from the telco services? These would typically drive and dominate the capital investments and, as such, should relate strongly to the Capex plans of telcos. It is customary to benchmark capital spending by comparing the Capex to Revenue (see Figure 8), indicating how much a business needs to invest into infrastructure and services to obtain a certain income level. If nothing is stated, the revenue used for the Capex-to-Revenue ratio would be total revenue. For telcos with fixed and mobile businesses, it’s a very high-level KPI that does not allow for too many insights (in my opinion). It requires some de-averaging to become more meaningful.

THE TELCO TECHNOLOGY FACTORY

Figure 8 (below) illustrates the main capital investment areas and cost drivers for telecommunications operations with either a fixed broadband network, a mobile network, or both. Typically, around 90% of the capital expenditures will be invested into the technology factory comprising network infrastructure, products, services, and all associated with information technology. The remaining ca. 10% will be spent on non-technical infrastructures, such as shops, office space, and other non-tech tangible assets.

Figure 9 Telco Capex is spent across physical (or tangible) infrastructure assets, such as communications equipment, brick & mortar that hosts the equipment, and staff. Furthermore, a considerable amount of a telcos Capex will also go to human development work, e.g., for IT, products & services, either carried out directly by own staff or third parties (i.e., capitalized labor). The above illustrates the macro-levels that make out a mobile or fixed telecommunications network, and the most important areas Capex will be allocated to.

If we take the helicopter view on a telco’s network, we have the customer’s devices, either mobile devices (e.g., smartphone, Internet of Things, tablet, … ) or fixed devices, such as the customer premise equipment (CPE) and set-top box. Typically the broadband network connection to the customer’s premise would require a media converter or optical network terminator (ONT). For a mobile network, we have a wireless connection between the customer device and the radio access network (RAN), the cellular network’s most southern point (or edge). Radio access technology (e.g., 3G, 4G, or 5G) is very important determines for the customer experience. For a fixed network connection, we have fiber or coax (cable) or copper connecting the customer’s premise and the fixed network (e.g., street cabinet). Access (in general) follows the distribution of the customers’ locations and concentration, and their generated traffic is aggregated increasingly as we move north and up towards and into the core network. In today’s modern networks, big-fat-data broadband connections interconnect with the internet and big public data centers hosting both 3rd party and operator-provided content, services, and applications that the customer base demands. In many existing networks, data centers inside the operator’s own “walls” likewise will have service and application platforms that provide customers with more of the operator’s services. Such private data centers, including what is called micro data centers (μDCs) or edge DCs, may also host 3rd party content delivery networks that enable higher quality content services to a telco’s customer base due to a higher degree of proximity to where the customers are located compared to internet-based data centers (that could be located anywhere in the world).

Figure 10 illustrates break-out the details of a mobile as well as a fixed (fiber-based) network’s infrastructure elements, including the customers’ various types of devices.

Figure 10 illustrates that on a helicopter level, a fixed and a classical mobile network structure are reasonably similar, with the main difference of one network carrying the mobile traffic and the other the fixed traffic. The traffic in the fixed network tends to be at least ten larger than in the mobile network. They mainly differ in the access node and how it connects to the customer. For fixed broadband, the physical connection is established between, for example, the ONL (Optical Line Terminal) in the optical distribution network and ONT (Optical Line Terminal) at the customer’s home via a fiber line (i.e., wired). The wireless connection for mobile is between the Radio Node’s antenna and the end-user device. Note: AAS: Advanced Antenna System (e.g., MiMo, massive-MiMo), BBU: Base-band unit, CPE: Customer Premise Equipment, IOT: Internet of Things, IX: Internet Exchange, OLT: Optical Line Termination, and ONT: Optical Network Termination (same as ONU: Optical Network Unit).

From Figure 10 above, it should be clear that there are a lot of similarities between the mobile and fixed networks, with the biggest difference being that the mobile access network establishes a wireless connection to the customer’s devices versus the fixed access network physically wired connection to the device situated at the customer’s premises.

This is good news for fixed-mobile telecommunications operators as these will have considerable architectural and, thus, investment synergies due to those similarities. Although, the sad truth is that even today, many fixed-mobile telco companies, particularly incumbent, remain far away from having achieved fixed-mobile network harmonization and conversion.

Moreover, there are many questions to be asked as well as concerns when it comes to our industry’s Capex plans; what is the Capex required to accommodate data growth, are existing budgets allowing for sufficient network densification (to accommodate growth and quality), and what is the Capex trade-off between frequency spectrum acquisition, antenna technology, and site densification, how much Capex is justified to pursue the best network in a given market, what is the suitable trade-off between investing in fiber to the home and aggressive 5G deployment, should (incumbent) telco’s pursue fixed wireless access (FWA) and how would that impact their capital plans, what is the right antenna strategy, etc…

On a high level, I will provide guidance on many of the above questions, in this article and in forthcoming ones.

THE CAPEX STRUCTURE OF A TELECOM COMPANY.

When taking a macro look at Capex and not yet having a good idea about the breakdown between mobile and fixed investment levels, we are helped that on a macro level, the Capex categories are similar for a fixed and a mobile network. Apart from the last mile (access) in a fixed network is a fixed line (e.g., fiber, coax, or copper) and a wireless connection in a mobile network; the rest is comparable in nature and function. This is not surprising as a business with a fixed-mobile infrastructure would (should!) leverage the commonalities in transport and part of the access architecture.

In the fixed business, devices required to enable services on the fixed-line network at the fixed customers’ home (e.g., CPE, STB, …) are a capital expense driven by new customers and device replacement. This is not the case for mobile devices (i.e., an operational expense).

Figure 11 above illustrates the major Capex elements and their distribution defined by the median, lower and upper quantiles (the box), and lower and upper extremes (the whiskers) of what one should expect of various elements’ contribution to telco Capex. Note: CPE: Customer Premise Equipment, STB: Set-Top Box.

CUSTOMER PREMISE EQUIPMENT (CPE) & SET-TOP BOXES (STB) investments ARE between 10% to 20% of the TelEcoM Capex.

The capital investment level into Customer premise equipment (CPE) depends on the expected growth in the fixed customer base and the replacement of old or defective CPEs already in the fixed customer base. We would generally expect this to make out between 10% to 20% of the total Capex of a fixed-mobile telco (and 0% in a mobile-only business). When migrating from one access technology (e.g., copper/xDSL phase-out, coaxial cable) to another (e.g., fiber or hybrid coaxial cable), more Capex may be required. Similar considerations for set-top boxes (STB) replacement due to, for example, a new TV platform, non-compliance with new requirements, etc. Many Western European incumbents are phasing out their extensive and aging copper networks and replacing those with fiber-based networks. At the same time, incumbents may have substantial capital requirements phasing out their legacy copper-based access networks, the capital burden on other competitor telcos in markets where this is happening if such would have a significant copper-based wholesale relationship with the incumbent.

In summary, over the next five years, we should expect an increase in CPE-based Caped due to the legacy copper phase-out of incumbent fixed telcos. This will also increase the capital pressure in transport and access categories.

CPE & STB Capex KPIs: Capex share of Total and Capex per Gross Added Customer.

Capex modeling comment: Use your customer forecast model as the driver for new CPEs. Your research should give you an idea of the price range of CPEs used by your target fixed broadband business. Always include CPE replacement in the existing base and the gross adds for the new CPEs. Many fixed broadband retail businesses have been conservative in the capabilities of CPEs they have offered to their customer base (e.g., low-end cheaper CPEs, poor WiFi quality, ≤1Gbps), and it should be considered that these may not be sufficient for customer demand in the following years. An incumbent with a large install base of xDSL customers may also have a substantial migration (to fiber) cost as CPEs are required to be replaced with fiber cable CPEs. Due to the current supply chain and delivery issues, I would assume that operators would be willing to pay a premium for getting critical stock as well as having priority delivery as stock becomes available (e.g., by more expensive shipping means).

Core network & service platformS, including data centers, investments ARE between 8% to 12% of the telecom Capex.

Core network and service platforms should not take up more than 10% of the total Capex. We would regard anything less than 5% or more than 15% as an anomaly in Capital prioritization. This said, over the next couple of years, many telcos with mobile operations will launch 5G standalone core networks, which is a substantial change to the existing core network architecture. This also raises the opportunity for lifting and shifting from monolithic systems or older cloud frameworks to cloud-native and possibly migrating certain functions onto public cloud domains from one or more hyperscalers (e.g., AWS, Azure, Google). As workloads are moved from telco-owned data centers and own monolithic core systems, telco technology cost structure may change from what prior was a substantial capital expense to an operational expense. This is particularly true for software-related developments and licensing.

Another core network & service platform Capex pressure point may come from political or investor pressure to replace Chinese network elements, often far removed from obsolescence and performance issues, with non-Chinese alternatives. This may raise the Core network Capex level for the next 3 to 5 years, possibly beyond 12%. Alas, this would be temporary.

In summary, the following topics would likely be on the Capex priority list;

1. Life-cycle management investments (I like to call Business-as-Usual demand) into software and hardware maintenance, end-of-life replacements, growth (software licenses, HW expansions), and miscellaneous topics. This area tends to dominate the Capex demand unless larger transformational projects exist. It is also the first area to be de-prioritized if required. Working with Priority 1, 2, and 3 categorizations is a good Capital planning methodology. Where Priority 1 is required within the following budget year 1, Prio. 2 is important but can wait until year two without building up too much technical debt and Prio. 3 is nice to have and not expected to be required for the next two subsequent budget years.

2. 5G (Standalone, SA) Core Network deployment (timeline: 18 – 24 months).

3. Network cloudification, initially lift-and-shift with subsequent cloud-native transformation. The trigger point will be enabling the deployment of the 5G standalone (SA) core. Operators will also take the opportunity to clean up their data centers and network core location (timeline: 24 – 36 months).

4. Although edge computing data centers (DC) typically are supposed to support the radio access network (e.g., for Open-RAN), the capital assignment would be with the core network as the expertise for this resides here. The intensity of this Capex (if built by the operator, otherwise, it would be Opex) will depend on the country’s size and fronthaul/backhaul design. The investment trigger point would generally commence on Open-RAN deployment (e.g., 1&1 & Telefonica Germany). The edge DC (or μDC) would most like be standard container-sized (or half that size) and could easily be provided by independent towerco or specific edge-DC 3rd party providers lessening the Capex required for the telco. For smaller geographies (e.g., Netherlands, Denmark, Austria, …), I would not expect this item to be a substantial topic for the Capex plans. Mainly if Open-RAN is not being pursued over the next 5 – 10 years by mainstream incumbent telcos.

5. Chinese supplier replacement. The urgency would depend on regulatory pressure, whether compensation is provided (unlikely) or not, and the obsolescence timeline of the infrastructure in question. Given the high quality at very affordable economics, I expect this not to have the biggest priority and will be executed within timelines dictated more by economics and obsolescence timelines. In any case, I expect that before 2025 most European telcos will have phased out Chinese suppliers from their Core Networks, incl. any Service platforms in use today (timeline: max. 36 months).

6. Cybersecurity investments strengthen infrastructure, processes, and vital data residing in data centers, service platforms, and core network elements. I expect a substantial increase in Capex (and Opex) arising from the telco’s focus on increasing the cyber protection of their critical telecom infrastructure (timeline: max 18 months with urgency).

Core Capex KPIs: Capex share of Total (knowing the share, it is straightforward to get the Capex per Revenue related to the Core), Capex per Incremental demanded data traffic (in Gigabits and Gigabyte per second), Capex per Total traffic, Capex per customer.

Capex modeling comment: In case I have little specific information about an operator’s core network and service platforms, I would tend to model it as a Euro per Customer, Euro per-incremental customer, and Euro per incremental traffic. Checking that I am not violating my Capex range that this category would typically fall within (e.g., 8% to 12%). I would also have to consider obsolescence investments, taking, for example, a percentage of previous cumulated core investments. As mobile operators are in the process, or soon will be, of implementing a 5G standalone core, having an idea of the number of 5G customers and their traffic would be useful to factor that in separately in this Capex category.

Estimating the possible Capex spend on Edge-RAN locations, I would consider that I need ca. 1 μDC per 450 to 700 km2 of O-RAN coverage (i.e., corresponding to a fronthaul distance between the remote radio and the baseband unit of 12 to 15 km). There may be synergies between fixed broadband access locations and the need for μ-datacenters for an O-RAN deployment for an integrated fixed-mobile telco. I suspect that 3rd party towercos, or alike, may eventually also offer this kind of site solutions, possibly sharing the cost with other mobile O-RAN operators.

Transport – core, metro & aggregation investments are between 5% to 15% of Telecom Capex.

The transport network consists of an optical transport network (OTN) connecting all infrastructure nodes via optical fiber. The optical transport network extends down to the access layer from the Core through the Metro and Aggregation layers. On top, the IP network ensures logical connection and control flow of all data transported up and downstream between the infrastructure nodes. As data traffic is carried from the edge of the network upstream, it is aggregated at one or several places in the network (and, of course, disaggregated in the downstream direction). Thus, the higher the transport network, the more bandwidth is supported on the optical and the IP layers. Most of the Capex investment needs would ensure that sufficient optical and IP capacity is available, supporting the growth projections and new service requirements from the business and that no bottlenecks can occur that may have disastrous consequences on customer experience. This mainly comes down to adding cards and ports to the already installed equipment, upgrading & replacing equipment as it reaches capacity or quality limitations, or eventually becoming obsolete. There may be software license fees associated with growth or the introduction of new services that also need to be considered.

Figure 12 above illustrates (high-level) the transport network topology with the optical transport network and IP networking on top. Apart from optical and IP network equipment, this area often includes investments into IP application functions and related hardware (e.g., BNG, DHCP, DNS, AAA RADIUS Servers, …), which have not been shown in the above. In most cases, the underlying optical fiber network would be present and sufficiently scalable, not requiring substantial Capex apart from some repair and minor extensions. Note DWDM: Dense Wavelength-Division multiplexing is an optical fiber multiplexing technology that increases the bandwidth utilization of a FON, BNG: Border Network Gateway connecting subscribers to a network or an internet service providers (ISP) network, important in wholesale arrangements where a 3rd party provides aggregation and access. DHCP: Dynamic Host Configuration Protocol providing IP address allocation and client configurations. AAA: Authentication, Authorization, and Accounting of the subscriber/user, RADIUS: Remote Authentication Dial-In User Service (Server) providing the AAA functionalities.

Although many telcos operate fixed-mobile networks and might even offer fixed-mobile converged services, they may still operate largely separate fixed and mobile networks. It is not uncommon to find very different transport design principles as well as supplier landscapes between fixed and mobile. The maturity, when each was initially built, and technology roadmaps have historically been very different. The fixed traffic dynamics and data volumes are several times higher than mobile traffic. The geographical presence between fixed and mobile tends to be very different (unless the telco of interest is the incumbent with a considerable copper or HFC network). However, the biggest reason for this state of affairs has been people and technology organizations within the telcos resisting change and much more aggressive transport consolidation, which would have been possible.

The mobile traffic could (should!) be accommodated at least from the metro/aggregation layers and upstream through the core transport. There may even be some potential for consolidation on front and backhauls that are worth considering. This would lead to supplier consolidation and organizational synergies as the technology organizations converged into a fixed-mobile engineering organization rather than two separate ones.

I would expect the share of Capex to be on the higher end of the likely range and towards the 10+% at least for the next couple of years, mainly if fixed and mobile networks are being harmonized on the transport level, which may also create an opportunity reduce and harmonize the supplier landscape.

In summary, the following topics would likely be on the Capex priority list;

  1. Life-cycle management (business-as-usual) investments, accommodating growth including new service and quality requirements (annual business-as-usual). There are no indications that the traffic or mobile traffic growth rate over the next five years will be very different from the past. If anything, the 5-year CAGR is slightly decreasing.
  2. Consolidating fixed and mobile transport networks (timelines: 36 to 60 months, depending on network size and geography). Some companies are already in the process of getting this done.
  3. Chinese supplier replacement. To my knowledge, there are fewer regulatory discussions and political pressure for telcos to phase out transport infrastructure. Nevertheless, with the current geopolitical climate (and the upcoming US election in 2024), telcos need to consider this topic very carefully; despite economic (less competition, higher cost), quality, and possible innovation, consequences may result in a departure from such suppliers. It would be a natural consideration in case of modernization needs. An accelerated phase-out may be justified to remove future risks arising from geopolitical pressures.

While I have chosen not to include the Access transport under this category, it is not uncommon to see its budget demand assigned to this category, as the transport side of access (fronthaul and backhaul transport) technically is very synergetic with the transport considerations in aggregation, metro, and core.

Transport Capex KPIs: Capex share of Total, the amount of Capex allocated to Mobile-only and Fixed-only (and, of course, to a harmonized/converged evolved transport network), The Utilization level (if data is available or modeled to this level). The amount of Capex-spend on fiber deployment, active and passive optical transport, and IP.

Capex modeling comment: I would see whether any information is available on a number of core data centers, aggregation, and metro locations. If this information is available, it is possible to get an impression of both core, aggregation, and metro transport networks. If this information is not available, I would assume a sensible transport topology given the particularities of the country where the operator resides, considering whether the operator is an incumbent fixed operator with mobile, a mobile-only operation, or a mobile operator that later has added fixed broadband to its product portfolio. If we are not talking about a greenfield operation, most, if not all, will already be in place, and mainly obsolescence, incremental traffic, and possible transport network extensions would incur Capex. It is important to understand whether fixed-mobile operations have harmonized and integrated their transport infrastructure or large-run those independently of each other. There is substantial Capex synergy in operating an integrated transport network, although it will take time and Capex to get to that integration point.

Access investments are typically between 35% to 50% of the Telecom Capex.

Figure 13 (above) is similar to Figure 8 (above), emphasizing the access part of Fixed and Mobile networks. I have extended the mobile access topology to capture newer development of Open-RAN and fronthaul requirements with pooling (“centralizing”) the baseband (BBU) resources in an edge cloud (e.g., container-sized computing center). Fronthaul & Open-RAN poses requirements to the access transport network. It can be relatively costly to transform a legacy RAN backhaul-only based topology to an Open-RAN fronthaul-based topology. Open-RAN and fronthaul topologies for Greenfield deployments are more flexible and at least require less Capex and Opex. 

Mobile Access Capex.

I will define mobile access (or radio access network, RAN) as everything from the antenna on the site location that supports the customers’ usage (or traffic demand) via the active radio equipment (on-site or residing in an edge-cloud datacenter), through the fronthaul and backhaul transport, up to the point before aggregation (i.e., pre-aggregation). It includes passive and active infrastructure on-site, steal & mortar or storage container, front- and backhaul transport, data center software & equipment (that may be required in an edge data center), and any other hardware or software required to have a functional mobile service on whatever G being sold by the mobile operator.

Figure 14 above illustrates a radio access network architecture that is typically deployed by an incumbent telco supporting up to 4G and 5G. A greenfield operation on 5G (and maybe 4G) could (maybe should?) choose to disaggregate the radio access node using an open interface, allowing for a supplier mix between the remote radio head (RRH and digital frontend) at the site location and the centralized (or distributed) baseband unit (BBU). Fronthaul connects the antenna and RRH with a remote BBU that is situated at an edge-cloud data center (e.g., storage container datacenter unit = micro-data center, μDC). Due to latency constraints, the distance between the remote site and the BBU should not be much more than 10 km. It is customary to name the 5G new radio node a gNB (g-Node-B) like the 4G radio node is named eNB (evolved-Node-B).

When considering the mobile access network, it is good to keep in mind that, at the moment, there are at least two main flavors (that can be mixed, of course) to consider. (1) A classical architecture with the site’s radio access hardware and software from a single supplier, with a remote radio head (RRH) as well as digital frontend processing at or near the antenna. The radio nodes do not allow for mixing suppliers between the remote RF and the baseband. Radio nodes are connected to backhaul transmission that may be enabled by fiber or microwave radios. This option is simple and very well-proven. However, it comes with supplier lock-in and possibly less efficient use of baseband resources as these are likewise fixed to the radio node that the baseband unit is installed. (2) A new Open- or disaggregated radio access network (O-RAN), with the Antenna and RHH at the site location (the RU, radio unit in O-RAN), then connected via fronthaul (≤ 10 – 20 km distance) to a μDC that contains the baseband unit (the DU, distributed unit in O-RAN). The μDC would then be connected to the backhaul that connects northbound to the Central Unit (CU), aggregation, and core. The open interface between the RRH (and digital frontend) and the BBU allows different suppliers and hosts the RAN-specific software on common off-the-shelf (COTS) computing equipment. It allows (in theory) for better scaling and efficiency with the baseband resources. However, the framework has not been standardized by the usual bodies of standardization (e.g., 3GPP) and is not universally accepted as a common standard that all telco suppliers would adhere to. It also has not reached maturity yet (sort of obvious) and is currently (as of July 2022) seen to be associated with substantial cyber-security risks (re: maturity). It may be an interesting deployment model for greenfield operations (e.g., Rakuten Mobile Japan, Jio India, 1&1 Germany, Dish Mobile USA). The O-RAN options are depicted in Figure 15 below.

Figure 15 The above illustrates a generic Open RAN architecture starting with the Advanced Antenna System (AAS) and the Radio Unit (RU). The RU contains the functionality associated with the (OSI model) layer 1, partitioned into the lower layer 1 functions with the upper layer 1 functions possibly moved out of the RU and into the Distributed Unit (DU) connected via the fronthaul transport. The DU, which typically will be connected to several RUs, must ensure proper data link management, traffic control, addressing, and reliable communication with the RU (i.e., layer 2 functionalities). The distributed unit connects via the mid-haul transport link to the so-called Central Unit (CU), which typically will be connected to several DUs. The CU plays an important role in the overall ORAN architecture, acting as a central control and management vehicle that coordinates the operations of DUs and RUs, ensuring an efficient and effective operation of the ORAN network. As may be obvious, from the summary of its functionality, layer 3 functionalities reside in the CU. The Central Unit connects via backhaul, aggregation, and core transport to the core network.

For established incumbent mobile operators, I do not see Option (2) as very attractive, at least for the next 5 – 7 years when many legacy technologies (i.e., non-5G) remain to be supported. The main concern should be the maturity, lack of industry-wise standardization, as well as cost of transforming existing access transport networks into compliance with a fronthaul framework. Most likely, some incumbents, the “brave” ones, will deploy O-RAN for 1 or a few 5G bands and keep their legacy networks as is. Most incumbent mobile operators will choose (actually have chosen already) conventional suppliers and the classical topology option to provide their 5G radio access network as it has the highest synergy with the access infrastructure already deployed. Thus, if my assertion is correct, O-RAN will only start becoming mass-market mainstream in 5 to 7 years, when existing deployments become obsolete, and may ultimately become mass-market viable by the introduction of 6G towards the end of the twenties. The verdict is very much still out there, in my opinion.

Planning the mobile-radio access networks Capex requirements is not (that) difficult. Most of it can be mathematically derived and be easily assessed against growth expectations, expected (or targeted) network utilization (or efficiency), and quality. The growth expectations (should) come from consumer and retail businesses’ forecast of mobile customers over the next 3 to 5 years, their expected usage (if they care, otherwise technology should), or data-plan distribution (maybe including technology distributions, if they care. Otherwise, technology should), as well as the desired level of quality (usually the best).

Figure 16 above illustrates a typical cellular planning structural hierarchy from the sector perspective. One site typically has 3 sectors. One sector can have multiple cells depending on the frequency bands installed in the (multi-band) antennas. Massive MiMo antenna systems provide target cellular beams toward the user’s device that extend the range of coverage (via the beam). Very fast scheduling will enable beams to be switched/cycled to other users in the covered sector (a bit oversimplified). Typically, the sector is planned according to the cell utilization, thus on a frequency-by-frequency basis.

Figure 17 illustrates that most investment drivers can be approached as statistical distributions. Those distributions will tell us how much investment is required to ensure that a critical parameter X remains below a pre-defined critical limit Xc within a given probability (i.e., the proportion of the distribution exceeding Xc). The planning approach will typically establish a reference distribution based on actual data. Then based on marketing forecasts, the planners will evolve the reference based on the expected future usage that drives the planning parameter. Example: Let X be the customer’s average speed in a radio cell (e.g., in a given sector of an antenna site) in the busy hour. The business (including technology) has decided it will target 98% of its cells and should provide better than 10 Mbps for more than 50% of the active time a customer uses a given cell. Typically, we will have several quality-based KPIs, and the more breached they are, the more likely it will be that a Capex action is initiated to improve the customer experience.

Network planners will have access to much information down to the cell level (i.e., the active frequency band in a given sector). This helps them develop solid planning and statistical models that provide confidence in the extrapolation of the critical planning parameters as demand changes (typically increases) that subsequently drive the need for expansions, parameter adjustments, and other optimization requirements. As shown in Figure 17 above, it is customary to allow for some cells to breach a defined critical limit Xc, usually though it is kept low to ensure a given customer experience level. Examples of planning parameters could be cell (and sector) utilization in the busy hour, active concurrent users in cell (or sector), duration users spend at a or lower deemed poor speed level in a given cell, physical resource block (the famous PRB, try to ask what it stands for & what it means😉) utilization, etc.

The following topics would likely be on the Capex priority list;

  1. New radio access deployment Capex. This may be for building new sites for coverage, typically in newly built residential areas, and due to capacity requirements where existing sites can no longer support the demand in a given area. Furthermore, this Capex also covers a new technology deployment such as 5G or deploying a new frequency band requiring a new antenna solution like 3.X GHz would do. As independent tower infrastructure companies (towerco) increasingly are used to providing the required passive site infrastructure solution (e.g., location, concrete, or steel masts/towers/poles), this part will not be a Capex item but be charged as Opex back to the mobile operator. From a European mobile radio access network Capex perspective, the average cost of a total site solution, with active as well as passive infrastructure, should have been reduced by ca. 100 thousand plus Euro, which may translate into a monthly Opex charge of 800 to 1300 Euro per site solution. It should be noted that while many operators have spun off their passive site solutions to third parties and thus effectively reduced their site-related Capex, the cost of antennas has increased dramatically as operators have moved away from classical simple SiSo (Single-in Singe-out) passive antennas to much more advanced antenna systems supporting multiple frequency bands, higher-order antennas (e.g., MiMo) and recently also started deploying active antennas (i.e., integrated amplifiers). This is largely also driven by mobile operators commissioning more and more frequency bands on their radio-access sites. The planning horizon needs at least to be 2 years and preferably 3 to 5 years.
  2. Capex investments that accommodate anticipated radio access growth and increased quality requirements. It is normal to be between 18 – 24 months ahead of the present capacity demand overall, accepting no more than 2% to 5% of cells (in BH) to breach a critical specification limit. Several such critical limits would be used for longer-term planning and operational day-to-day monitoring.
  3. Life-cycle management (business-as-usual) investments such as software annual fees, including licenses that are typically structured around the technologies deployed (e.g., 2G, 3G, 4G, and 5G) and active infrastructure modernization replacing radio access equipment (e.g., baseband units, radio units, antennas, …) that have become obsolete. Site reworks or construction optimization would typically be executed (on request from the operator) by the Towerco entity, where the mobile operator leases the passive site infrastructure. Thus, in such instances may not be a Capex item but charged back as an Operational expense to the telco.
  4. Even if there have been fewer regulatory discussions and political pressure for telcos to phase out radio access, Chinese supplier replacement should be considered. Nevertheless, with the current geopolitical climate (and the upcoming US election), telcos need to consider this topic very carefully; despite economic (less competition, higher cost), quality, and possible innovation, consequences may result in a departure from such suppliers. It would be a natural consideration in case of modernization needs. An accelerated phase-out may be justified to remove future risks arising from geopolitical pressures, although it would result in above-and-beyond capital commitment over a shorter period than otherwise would be the case. Telco valuation may suffer more in the short to medium term than otherwise would have been the case with a more natural phaseout due to obsolescence.

Mobile Access Capex KPIs: Capex share of Total, Access Utilization (reported/planned data traffic demand to the data traffic that could be supplied if all or part of the spectrum was activated), Capex per Site location, Capex per Incremental data traffic demand (in Gigabyte and Gigabit per second which is the real investment driver), Capex per Total Traffic (in Gigabyte and Gigabit per second), Capex per Mobile Customer and Capex to Mobile Revenue (preferably service revenue but the total is fine if the other is not available). As a rule of thumb, 50% of a mobile network typically covers rural areas, which also may carry less than 20% of the total data traffic.

Whether actual and planned Capex is available or an analyst is modeling it, the above KPIs should be followed over an extended period. A single year does not tell much of a story.

Capex modeling comment: When modeling the Capex required for the radio access network, you need to have an idea about how many sites your target telco has. There are many ways to get to that number. In most European countries, it is a matter of public record. Most telcos, nowadays, rarely build their own passive site infrastructure but get that from independent third-party tower companies (e.g., CellNex w. ca. 75k locations, Vantage Towers w. ca. 82k locations, … ) or site-share on another operators site locations if available. So, modeling the RAN Capex is a matter of having a benchmark of the active equipment, knowing what active equipment is most likely to be deployed and how much. I see this as being an iterative modeling process. Given the number of sites and historical Capex, it is possible to come to a reasonable estimate of both volumes of sites being changed and the range of unit Capex (given good guestimates of active equipment pricing range). Of course, in case you are doing a Capex review, the data should be available to you, and the exercise should be straightforward. The mobile Capex KPIs above will allow for consistency checks of a modeling exercise or guide a Capex review process.

I recommend using the classical topology described above when building a radio access model. That is unless you have information that the telco under analysis is transforming to a disaggregated topology with both fronthaul and backhaul. Remember you are not only required to capture the Capex for what is associated with the site location but also what is spent on the access transport. Otherwise, there is a chance that you over-estimate the unit-Capex for the site-related investments.

It is also worth keeping in mind that typically, the first place a telecom company would cut Capex (or down-prioritize) is pressured during the planning process would be in the radio access network category. The reason is that the site-related unitary capex tends to be incredibly well-defined. If you reduce your rollout to 100 site-related units, you should have a very well-defined quantum of Capex that can be allocated to another category. Also, the operational impact of cutting in this category tends to be very well-defined. Depending on how well planned the overall Capex has been done, there typically would be a slack of 5% to 10% overall that could be re-assigned or ultimately reduced if financial results warrant such a move.

Fixed Access Capex.

As mobile access, fixed access is about getting your service out to your customers. Or, if you are a wholesale provider, you can provide the means of your wholesale customer reaching their customer by providing your own fixed access transport infrastructure. Fixed access is about connecting the home, the office, the public institution (e.g., school), or whatever type of dwelling in general.

Figure 18 illustrates a fixed access network and its position in the overall telco architecture. The following make up the ODN (Optical Distribution Network); OLT: Optical Line Termination, ODF: Optical Distribution Frame, POS: Passive Optical Splitter, ONT: Optical Network Termination. At the customer premise, besides the ONT, we have the CPE: Customer Premise Equipment and the STB: Set-Top Box. Suppose you are an operator that bought wholesale fixed access from another telco’ (incl. Open Access Providers, OAPs). In that case, you may require a BNG to establish the connection with your customer’s CPE and STB through the wholesale access network.

As fiber optical access networks are being deployed across Europe, this tends to be a substantial Capex item on the budgets of telcos. Here we have two main Capex drivers. First is the Capex for deploying fibers across urban areas, which provides coverage for households (or dwellings) and is measured as Capex-per-homes passed. Second is the Capex required for establishing the connection to households (or dwellings). The method of fiber deployment is either buried, possibly using existing ducts or underground passageways, or via aerial deployment using established poles (e.g., power poles or street furniture poles) or new poles deployed with the fiber deployment. Aerial deployment tends to incur lower Capex than buried fiber solutions due to requiring less civil work. The OLT, ODF, POS, and optical fiber planning, design, and build to provide home coverage depends on the home-passed deployment ambition. The fiber to connect a home (i.e., civil work and materials), ONT, CPE, and STBs are driven by homes connected (or FTTH connected). Typically, CPE and STBs are not included in the Access Capex but should be accounted for as a separate business-driven Capex item.

The network solutions (BNG, OLT, Routers, Switches, …) outside the customer’s dwelling come in the form of a cabinet and appropriate cards to populate the cabinet. The cards provide the capacity and serviced speed (e.g., 100 Mbps, 300 Mbps, 1 Gbps, 10 Gbps, …) sold to the fixed broadband customer. Moreover, for some of the deployed solutions, there is likely a mandatory software (incl. features) fee and possibly both optional and custom-specific features (although rare to see that in mainstream deployments). It should be clear (but you would be surprised) that ONT and CPE should support the provisioned speed of the fixed access network. The customer cannot get more quality than the minimum level of either the ONT, CPE, or what the ODN has been built to deliver. In other words, if the networking cards have been deployed only to support up to 1 Gbps and your ONT, and CPE may support 3 Gbps or more, your customer will not be able to have a service beyond 1 Gbps. Of course, the other way around as well. I cannot stress enough the importance of longer-term planning in this respect. Your network should be as flexible as possible in providing customer services. It may seem that Capex savings can be made by only deploying capacity sold today or may be required by business over the next 12 months. While taking a 3 to 5-year view on the deployed network capacity and ONT/CPEs provided to customers avoids having to rip out relatively new equipment or finance the significant replacement of obsolete customer premise equipment that no longer can support the services required.

When we look at the economic drivers for fixed access, we can look at the capital cost of deploying a kilometer of fiber. This is particularly interesting if we are only interested in the fiber deployment itself and nothing else. Depending on the type of clutter, deployment, and labor cost occur. Maybe it is more interesting to bundle your investment into what is required to pass a household and what is required to connect a household (after it has been passed). Thus, we look at the Capex-per-home (or dwellings) passed and separate the Capex to connect an individual customer’s premise. It is important to realize that these Capex drivers are not just a single value but will depend on the household density depends on the type of area the deployment happens. We generally expect dense urban clutters to have a high dwelling density; thus, more households are covered (or passed) per km of fiber deployed. Dense-urban areas, however, may not necessarily hold the highest density of potential residential customers and hold less retail interest in the retail business. Generally, urban areas have higher household densities (including residential households) than sub-urban clutter. Rural areas are expected to have the lowest density and, thus, the most costly (on a household basis) to deploy.

Figure 19, just below, illustrates the basic economics of buried (as opposed to aerial) fiber for FTTH homes passed and FTTH homes connected. Apart from showing the intuitive economic logic, the cost per home passed or connected is driven by the household density (note: it’s one driver and fairly important but does not capture all the factors). This may serve as a base for rough assessments of the cost of fiber deployment in homes passed and homes connected as a function of household density. I have used data in the Fiber-to-the-Home Council Europe report of July 2012 (10 years old), “The Cost of Meeting Europe’s Network Needs”, and have corrected for the European inflationary price increase since 2012 of ca. 14% and raised that to 20% to account for increased demand for FTTH related work by third parties. Then I checked this against some data points known to me (which do not coincide with the cities quoted in the chart). These data points relate to buried fiber, including the homes connected cost chart. Aerial fiber deployment (including home connected) would cost less than depicted here. Of course, some care should be taken in generalizing this to actual projects where proper knowledge of the local circumstances is preferred to the above.

Figure 19 The “chicken and egg” of connecting customers’ premises with fiber and providing them with 100s of Mbps up to Gbps broadband quality is that the fibers need to pass the home first before the home can be connected. The cost of passing a premise (i.e., the home passed) and connecting a premise (home connected) should, for planning purposes, be split up. The cost of rolling out fiber to get homes-passed coverage is not surprisingly particularly sensitive to household density. We will have more households per unit area in urban areas compared to rural areas. Connecting a home is more sensitive to household density in deep rural areas where the distance from the main fiber line connection point to the household may be longer. The above cost curves are for buried fiber lines and are in 2021 prices.

Aerial fiber deployment would generally be less capital-intensive due to faster and easier deployment (less civil work, including permitting) using pre-existing (or newly built) poles. Not every country allows aerial deployment or even has the infrastructure (i.e., poles) available, which may be medium and low-voltage poles (e.g., for last-mile access). Some countries will have a policy allowing only buried fibers in the city or metropolitan areas and supporting pole infrastructure for aerial deployment in sub-urban and rural clutters. I have tried to illustrate this with Figure 18 below, where the pie charts show the aerial potential and share that may have to be assigned to buried fiber deployment.

Figure 20 above illustrates the amount of fiber coverage (i.e., in terms of homes passed) in Western European markets. The number for 2015 and 2021 is based on European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.). The 2025 & 2031 coverage numbers are my extrapolation of the 5-year trend leading up to 2021, considering the potential for aerial versus buried deployment. Aerial making accelerated deployment gains is more likely than in markets that only have buried fiber as a possibility, either because of regulation or lack of appropriate infrastructure for aerials. The only country that may be below 50% FTTH coverage in 2025 is Germany (i.e., DE), with a projected 39% of homes passed by 2025. Should Germany aim for 50% instead, they would have to do ca. 15 million households passed or, on average, 3 million a year from 2021 to 2025. Maximum Germany achieved in one year was in 2020, with ca. 1.4 million homes passed (i.e., Covid was good for getting “things done”). In 2021 this number dropped to ca. 700 thousand or half of the 2020 number. The maximum any country in Europe has done in one year was France, with 2.9 million homes passed in 2018. However, France does allow for aerial fiber deployment outside major metropolitan areas.

Figure 21 above provides an overview across Western Europe for the last 5 years (2016 – 2021) of average annual household fiber deployment, the maximum done in one year in the previous 5 years, and the average required to achieve household coverage in 2026 shown above in Figure 20. For Germany (DE), the average deployment pace of 3.23 homes passed per year (orange bar) would then result in a coverage estimate of 25%. I don’t see any practical reasons for the UK, France, and Italy not to make the estimated household coverage by 2026, which may exceed my estimates.

From a deployment pace and Capex perspective, it is good to keep in mind that as time goes by, the deployment cost per household is likely to increase as household density reduces when the deployment moves from metropolitan areas toward suburban and rural. Thus, even if the deployment pace may reduce naturally for many countries in Figure 20 towards 2025, absolute Capex may not necessarily reduce accordingly.

In summary, the following topics would likely be on the Capex priority list;

  1. Continued fiber deployment to achieve household coverage. Based on Figure 17, at household (HH) densities above 500 per km2, the unit Capex for buried fiber should be below 900 Euro per HH passed with an average of 600 Euro per HH passed. Below 500 HH per km2, the cost increases rapidly towards 3,000 Euro per HH passed. The aerial deployment will result in substantially lower Capex, maybe with as much as 50% lower unit Capex.
  2. As customers subscribe, the fiber access cost associated with connecting homes (last-mile connectivity) will need to be considered. Figure 17 provides some guidance regarding the quantum-Euro range expected for buried fiber. Aerial-based connections may be somewhat cheaper.
  3. Life-cycle management (business-as-usual) investments, modernization investments, accommodating growth including new service and quality requirements (annual business as usual). Typically it would be upgrading OLT, ONTs, routers, and switches to support higher bandwidth requirements upgrading line cards (or interface cards), and moving from ≤100 Mbps to 1 Gbps and 10 Gbps. Many telcos will be considering upgrading their GPON (Gigabit Passive Optical Networks, 2.5 Gbps↓ / 1.2 Gbps↑) to provide XGPON (10 Gbps↓ / 2.5 Gbps↑) or even XGSPON services (10 Gbps↓ / 10 Gbps↑).
  4. Chinese supplier exposure and risks (i.e., political and regulatory enforcement) may be an issue in some Western European markets and require accelerated phase-out capital needs. In general, I don’t see fixed access infrastructure being a priority in this respect, given the strong focus on increasing household fiber coverage, which already takes up a lot of human and financial resources. However, this topic needs to be considered in case of obsolescence and thus would be a business case and performance-driven with a risk adjustment in dealing with Chinese suppliers at that point in time.

Fixed Access Capex KPIs: Capex share of Total, Capex per km, Number of HH passed and connected, Capex per HH passed, Capex per HH connected, Capex to Incremental Traffic, GPON, XGPON and XGSPON share of Capex and Households connected.

Whether actual and planned Capex is available or an analyst is modeling it, the above KPIs should be followed over an extended period. A single year does not tell much of a story.

Capex modeling comment: In a modeling exercise, I would use estimates for the telco’s household coverage plans as well as the expected household-connected sales projections. Hopefully, historical numbers would be available to the analyst that can be used to estimate the unit-Capex for a household passed and a household connected. You need to have an idea of where the telco is in terms of household density, and thus as time goes by, you may assume that the cost of deployment per household increases somewhat. For example, use Figure 18 to guide the scaling curve you need. The above-fixed access Capex KPIs should allow checking for inconsistencies in your model or, if you are reviewing a Capex plan, whether that Capex plan is self-consistent with the data provided.

If anyone would have doubted it, there is still much to do with fiber optical deployment in Western Europe. We still have around 100+ million homes to pass and a likely capital investment need of 100+ billion euros. Fiber deployment will remain a tremendously important investment area for the foreseeable future.

Figure 22 shows the remaining fiber coverage in homes passed based on 2021 actuals for urban and rural areas. In general, it is expected that once urban areas’ coverage has reached 80% to 90%, the further coverage-based rollout will reduce. Though, for attractive urban areas, overbuilt, that is, deploying fiber where there already are fibers deployed, is likely to continue.

Figure 23 The top illustrates the next 5 years’ weekly rollout to reach an 80% to 90% household coverage range by 2025. The bottom, it shows an estimate of the remaining capital investment required to reach that 80% to 90% coverage range. This assessment is based on 2021 actuals from the European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.); the weekly activity and Capex levels are thus from 2022 onwards.

In many Western European countries, the pace is expected to be increased considerably compared to the previous 5 years (i.e., 2016 – 2021). Even if the above figure may be over-optimistic, with respect to the goal of 2026, the European ambition for fiberizing European markets will impose a lot of pressure on speedy deployment.

IT investment levels are typically between 15% and 25% of Telecom Capex.

IT may be the most complex area to reach a consensus on concerning Capex. In my experience, it is also the area within a telco with the highest and most emotional discussion overhead within the operations and at a Board level. Just like everyone is far better at driving a car than the average driver, everyone is far better at IT than the IT experts and knows exactly what is wrong with IT and how to make IT much better and much faster, and much cheaper (if there ever was an area in telco-land where there are too many cooks).

Why is that the case? I tend to say that IT is much more “touchy-feely” than networks where most of the Capex can be estimated almost mathematically (and sufficiently complicated for non-technology folks to not bother with it too much … btw I tend to disagree with this from a system or architecture perspective). Of course, that is also not the whole truth.

IT designs, plans, develops (or builds), and operates all the business support systems that enable the business to sell to its customers, support its customers, and in general, keep the relationship with the customer throughout the customer life-cycle across all the products and services offered by the business irrespective of it being fixed or mobile or converged. IT has much more intense interactions with the business than any other technology department, whose purpose is to support the business in enabling its requirements.

Most of the IT Capex is related to people’s work, such as development, maintenance, and operations. Thus capitalized labor of external and internal labor is the main driver for IT Capex. The work relates to maintaining and improving existing services and products and developing new ones on the IT system landscape or IT stacks. In 2021, Western European telco Capex spending was about 20% of their total revenue. Out of that, 4±1 % or in the order of 10±3 billion Euro is spent on IT. With ca. 714 million fixed and mobile subscribers, this corresponds to an IT average spend of 14 Euros per telco customer in 2021. Best investment practices should aim at an IT Capex spend at or below 3% of revenue on average over 5 years (to avoid penalizing IT transformation programs). As a rule of thumb, if you do not have any details of internal cost structure (I bet you usually would not have that information), assume that the IT-related Opex has a similar quantum as Capex (you may compensate for GDP differences between markets). Thus, the total IT spend (Capex and Opex) would be in the order of 2×Capex, so the IT Spend to Revenue double the IT-related Capex to Revenue. While these considerations would give you an idea of the IT investment level and drill down a bit further into cost structure details, it is wise to keep in mind that it’s all a macro average, and the spread can be pretty significant. For example, two telcos with roughly the same number of customers, IT landscape, and complexity and have pretty different revenue levels (e.g., due to differences in ARPU that can be achieved in the particular market) may have comparable absolute IT spending levels but very different relative levels compared to the revenue. I also know of telcos with very low total IT spend to Revenue ITR (shareholder imposed), which had (and have) a horrid IT infrastructure performance with very extended outages (days) on billing and frequent instabilities all over its IT systems. Whatever might have been saved by imposing a dramatic reduction in the IT Capex (e.g., remember 10 million euros Capex reduction equivalent to 200 million euros value enhancement) was more than lost on inferior customer service and experience (including the inability to bill the customers).

You will find industry experts and pundits that expertly insist that your IT development spend is way too high or too low (although the latter is rare!). I recommend respectfully taking such banter seriously. Although try to understand what they are comparing with, what KPIs they are using, and whether it’s apples for apples and not with pineapples. In my experience, I would expect a mobile-only business to have a better IT spend level than a fixed-mobile telco, as a mobile IT landscape tends to be more modern and relatively simple compared to a fixed IT landscape. First, we often find more legacy (and I mean with a capital L) in the fixed IT landscape with much older services and products still being kept operational. The fixed IT landscape is highly customized, making transformation and modernization complex and costly. At least if old and older legacy products must remain operational. Another false friend in comparing one company IT spending with another’s is that the cost structure may be different. For example, it is worth understanding where OSS (Operational Support System) development is accounted for. Is it in the IT spend, or is it in the Network-side of things? Service platforms and Data Centers may be another difference where such spending may be with IT or Networks.

Figure 24 shows the helicopter view of a traditional telco IT architectural stack. Unless the telco is a true greenfield, it is a very normal state of affairs to have multiple co-existing stacks, which may have some degree of integration at various levels (sub-layers). Most fixed-mobile telcos remain with a high degree of IT architecture separation between their mobile and fixed business on a retail and B2B level. When approaching IT, investments never consider just one year. Understand their IT investment strategy in the immediate past (2-3 years prior) as well as how that fits with known and immediate future investments (2 – 3 years out).

Above, Figure 24 illustrates the typical layers and sub-layers in an IT stack. Every sub-layer may contain different applications, functionalities, and systems, all with an over-arching property of the sub-layer description. It is not uncommon for a telco to have multiple IT stacks serving different brands (e.g., value, premium, …) and products (e.g., mobile, fixed, converged) and business lines (e.g., consumer/retail, business-to-business, wholesale, …). Some layers may be consolidated across stacks, and others may be more fragmented. The most common division is between fixed and mobile product categories, as historically, the IT business support systems (BSS) as well as the operational support systems (OSS) were segregated and might even have been managed by two different IT departments (that kind of silliness is more historical albeit recent).

Figure 25 shows a typical fixed-mobile incumbent (i.e., anything not greenfield) multi-stack IT architecture and their most likely aspiration of aggressive integrated stack supporting a fixed-mobile conversion business. Out of experience, I am not a big fan of retail & B2B IT stack integration. It creates a lot of operational complexity and muddies the investment transparency and economics of particular B2B at the expense of the retail business.

A typical IT landscape supporting fixed and mobile services may have quite a few IT stacks and a wide range of solutions for various products and services. It is not uncommon that a Fixed-Mobile telco would have several mobile brands (e.g., premium, value, …) and a separate (from an IT architecture perspective, at least) fixed brand. Then in addition, there may be differences between the retail (business-to-consumer, B2C) and the business-to-business (B2B) side of the telco, also being supported by separate stacks or different partitions of a stack. This is illustrated in Figure 24 above. In order for the telco business to become more efficient with respect to its IT landscape, including development, maintenance, and operational aspects of managing a complex IT infrastructure landscape, it should strive to consolidate stacks where it makes sense and not un-importantly along the business wish of convergence at least between fixed and mobile.

Figure 24 above illustrates an example of an IT stack harmonization activity long retail brands as well as Fixed and Mobile products as well as a separation of stacks into a retail and a business-to-business stack. It is, of course, possible to leverage some of the business logic and product synergies between B2C and B2B by harmonizing IT stacks across both business domains. However, in my experience, nothing great comes out of that, and more likely than not, you will penalize B2C by spending above and beyond value & investment attention on B2B. The B2B requirements tend to be significantly more complex to implement, their specifications change frequently (in line with their business customers’ demand), and the unit cost of development returns less unit revenue than the consumer part. Economically and from a value-consideration perspective, the telco needs to find an IT stack solution that is more in line with what B2B contributes to the valuation and fits its requirements. That may be a big challenge, particularly for minor players, as its business rarely justifies a standalone IT stack or developments. At least not a stack that is developed and maintained at the same high-quality level as a consumer stack. There is simply a mismatch in the B2B requirements, often having much higher quality and functionality requirements than the consumer part, and what it contributes to the business compared to, for example, B2C.

When I judge IT Capex, I care less about the absolute level of spend (within reason, of course) than what is practical to support within the given IT landscape the organization has been dealt with and, of course, the organization itself, including 3rd party support. Most systems will have development constraints and a natural order of how development can be executed. It will not matter how much money you are given or how many resources you throw at some problems; there will be an optimum amount of resources and time required to complete a task. This naturally leads to prioritization which may lead to disappointment of some stakeholders and projects that may not be prioritized to the degree they might feel entitled to.

When looking at IT capital spending and comparing one telco with another, it is worthwhile to take a 3- to 5-year time horizon, as telcos may be in different business and transformation cycles. A one-year comparison or benchmark may not be appropriate for understanding a given IT-spend journey and its operational and strategic rationale. Search for incidents (frequency and severity) that may indicate inappropriate spend prioritization or overall too little available IT budget.

The IT Capex budget would typically be split into (a) Consumer or retail part (i.e., B2C), (b) Business to Business and wholesale part, (c) IT technical part (optimization, modernization, cloudification, and transformations in general), and a (d) General and Administrative (G&A) part (e.g., Finance, HR, ..). Many IT-related projects, particularly of transformative nature, will run over multiple years (although if much more than 24 months, the risk of failure and monetary waste increases rapidly) and should be planned accordingly. For the business-driven demand (from the consumer, business, and wholesale), it makes sense to assign Capex proportional to the segment’s revenue and the customers those segments support and leverage any synergies in the development work required by the business units. For IT, capital spending should be assigned, ensuring that technical debt is manageable across the IT infrastructure and landscape and that efficiency gains arising from transformative projects (including landscape modernization) are delivered timely. In general, such IT projects promise efficiency in terms of more agile development possibilities (faster time to market), lower development and operational costs, and, last but not least, improved quality in terms of stability and reduced incidents. The G&A prioritizes finance projects and then HR and other corporate projects.

In summary, the following topics would likely be on the Capex priority list;

  1. Provide IT development support for business demand in the next business plan cycle (3 – 5 years with a strong emphasis on the year ahead). The allocation key should be close to the Revenue (or Ebitda) and customer contribution expected within the budget planning period. The development focus is on maintenance, (incremental) improvements to existing products/services, and new products/services required to make the business plans. In my experience, the initial demand tends to be 2 to 3 times higher than what a reasonable financial envelope would dictate (i.e., even considering what is possible to do within the natural limitations of the given IT landscape and organization) and what is ultimately agreed upon.
  2. Cloudification transformation journey moving away from the traditional monolithic IT platform and into a public, hybrid, or private cloud environment. In my opinion, the safest approach is a “lift-and-shift” approach where existing functionality is developed in the cloud environment. After a successful migration from the traditional monolithic platform into the cloud environment, the next phase of the cloudification journey will be to move to a cloud-native framework should be embarked. This provides a very solid automation framework delivering additional efficiencies and improved stability and quality (e.g., reduction in incidents). Analysts should be aware that migrating to a (public) cloud environment may reduce the capitalization possibilities with the consequence that Capex may reduce in the forward budget planning, but this would be at the expense of increased Opex for the IT organization.
  3. Stack consolidation. Reducing the number of IT stacks generally lowers the IT Capex demand and improves development efficiency, stability, and quality. The trend is to focus on the harmonization efforts on the frontend (Portals and Outlets layer in Figure 14), the CRM layer (retiring legacy or older CRM solutions), and moving down the layers of the IT stack (see Figure 14) often touching the complex backend systems when they become obsolete providing an opportunity to migrate to a modern cloud-based solution (e.g., cloud billing).
  4. Modernization activities are not covered by cloudification investments or business requirements.
  5. Development support for Finance (e.g., ERP/SAP requirements), HR requirements, and other miscellaneous activities not captured above.
  6. Chinese suppliers are rarely an issue in Western European telecom’s IT landscape. However, if present in a telco’s IT environment, I would expect Capex has been allocated for phasing out that supplier urgently over the next 24 months (pending the complexity of such a transformation/migration program) due to strong political and regulatory pressures. Such an initiative may have a value-destructing impact as business-driven IT development (related to the specific system) might not be prioritized too highly during such a program and thus result in less ability to compete for the telco during a phase-out program.

IT Capex KPIs: IT share of Total Capex (if available, broken down into a Fixed and Mobile part), IT Capex to Revenue, ITR (IT total spend to Revenue), IT Capex per Customer, IT Capex per Employee, IT FTEs to Total FTEs.

Moreover, if available or being modeled, I would like to have an idea about how much of the IT Capex goes to investment categories such as (i) Maintain, (ii) Growth, and (iii) Transform. I will get worried if the majority of IT Capex over an extended period goes to the Growth category and little to Maintain and Transform. This indicates a telco that has deprioritized quality and ignores efficiency, resulting in the risk of value destruction over time (if such a trend were sustained). A telco with little Transform spend (again over an extended period) is a business that does not modernize (another word for sweating assets).

Capex modeling comment: when I am modeling IT and have little information available, I would first assume an IT Capex to Revenue ratio around 4% (mobile-only) to 6% (fixed-mobile operation) and check as I develop the other telco Capex components whether the IT Capex stays within 15% to 25%. Of course, keep an eye out for all the above IT Capex KPIs, as they provide a more holistic picture of how much confidence you can have in the Capex model.

Figure 26 illustrates the anticipated IT Capex to Revenue ranges for 2024: using New Street Research (total) Capex data for Western Europe, the author’s own Capex projection modeling, and using the heuristics that IT spend typically would be 15% to 25% of the total Capex, we can estimate the most likely ranges of IT Capex to Revenue for the telecommunications business covered by NSR for 2024. For individual operations, we may also want to look at the time series of IT spending to revenue and compare that to any available intelligence (e.g., transformation intensive, M&A integration, business-as-usual, etc..)

Using the heuristic of the IT Capex being between 15% (1st quantile) and 25% (3rd quantile) of the total Capex, we can get an impression of how much individual Telcos invest in IT annually. The above chart shows such an estimate for 2024. I have the historical IT spending levels for several Western European Telcos, which agree well with the above and would typically be a bit below the median unless a Telco is in the progress of a major IT transformation (e.g., after a merger, structural separation, Huawei forced replacement, etc..). One would also expect and should check that the total IT spend, Capex and Opex, are decreasing over time when the transformational IT spend has been removed. If this is observed, it would indicate that Telco does become increasingly more efficient in its IT operation. Usually, the biggest effect should be in IT Opex reduction over time.

Figure 27 illustrates the anticipated IT Capex to Customer ranges for 2024: having estimated the likely IT spend ranges (in Figure 26) for various Western European telcos, allows us to estimate the expected 2024 IT spend per customer (using New Street Research data, author’s own Capex projection model and the IT heuristics describe in the section). In general and in the absence of structural IT transformation programs, I would expect the IT per customer spend to be below the median. Some notes to the above results: TDC (Nuuday & TDC Net) has major IT transformation programs ongoing after the structural separation, KPN is in progress with replacing their Huawei BSS, and I would expect them to be at the upper part of IT spending, Telenor Norway seems higher than I would expect but is an incumbent that traditionally spends substantially more than its competitors so might be okay but caution should be taken here, Switzerland in general and Swisscom, in particular, is higher than I would have expected. This said, it is a sophisticated Telco services market that would be likely to spend above the European average, irrespective I would take some caution with the above representation for Switzerland & Swisscom.

Similar to the IT Capex to Revenue, we can get an impression of what Telcos spend on IT Capex as it relates to their total mobile and fixed customer base. Again for Telcos in Western Europe (as well as outside), these ranges shown above do seem reasonable as the estimated range of where one would expect the IT spend. The analyst is always encouraged to look at this over a 3- to 5-year period to better appreciate the trend and should keep in mind that not all Telcos are in synch with their IT investments (as hopefully is obvious as transformation strategies and business cycles may be very different even within the same market).

Other, or miscellaneous, investments tend to be between 3% and 8% of the Telecom Capex.

When modeling a telco’s Capex, I find it very helpful to keep an “Other” or “Miscellaneous” Capex category for anything non-technology related. Modeling-wise, having a placeholder for items you don’t know about or may have forgotten is convenient. I typically start my models with 15% of all Capex. As my model matures, I should be able to reduce this to below 10% and preferably down to 5% (but I will accept 8% as a kind of good enough limit). I have had Capx review assignments where the Capex for future years had close to 20% in the “Miscellaneous.” If this “unspecified” Capex would not be included, the Capex to Revenue in the later years would drop substantially to a level that might not be deemed credible. In my experience, every planned Capex category will have a bit of “Other”-ness included as many smaller things require Capex but are difficult to mathematically derive a measure for. I tend to leave it if it is below 5% of a given Capex category. However, if it is substantial (>5%), it may reveal “sandbagging” or simply less maturity in the Capex planning and budget process.

Apart from a placeholder for stuff we don’t know, you will typically find Capex for shop refurbishment or modernization here, including office improvements and IT investments.

DE-AVERAGING THE TELECOM CAPEX TO FIXED AND MOBILE CONTRIBUTIONS.

There are similar heuristics to go deeper down into where the Capex should be spent, but that is a detail for another time.

Our first step is decomposing the total Capex into a fixed and a mobile component. We find that a multi-linear model including Total Capex, Mobile Customers, Mobile Service Revenue, Fixed Customers, and Fixed Service Revenues can account for 93% of the Capex trend. The multi-linear regression formula looks like the following;

with C = Capex, N = total customer count, R = service revenue, and α and β are the regression coefficient estimates from the multi-linear regression. The Capex model has been trained on 80% of the data (1,008 data points) chosen randomly and validated on the remainder (252 data points). All regression coefficients (4 in total) are statistically significant, with p-values well below a 95% confidence level.

Figure 28 above shows the Predicted Capex versus the Actual Capex. It illustrates that the predicted model agreed reasonably well with the actual Capex, which would also be expected based on the statistical KPIs resulting from the fit.

The Total is (obviously) available to us and therefore allows us to estimate both fixed and mobile Capex levels, by

The result of the fixed-mobile Capex decomposition is shown in Figure 26 below. Apart from being (reasonably) statistically sound, it is comforting that the trend in Capex for fixed and mobile seem to agree with what our intuition should be. The increase in mobile Capex (for Western Europe) over the last 5 years appears reasonable, given that 5G deployment commenced in early 2019. During the Covid lockdown from early 2020, fixed revenue was boosted by a massive shift in fixed broadband traffic (and voice) from the office to the individuals’ homes. Likewise, mobile service revenues have been in slow decline for years. Thus, the Capex increase due to 5G and reduced mobile service revenues ultimately leads to a relatively more significant increase in the mobile Capex to Revenue ratio.

Figure 29 illustrates the statistical modeling (by multi-linear regression), or decomposition, of the Total Capex as a function of Mobile Customers, Mobile Service Revenues, Fixed Customers, and Fixed Service Revenues, allowing to break up of the Capex into Fixed and Mobile components by decomposing the total Capex. The absolute Capex level is higher for fixed than what is found for mobile, with about a factor of 2 until 2021, when mobile Capex increases due to 5G investments in the mobile industry. It is found that the Mobile Capex has increased the most over the last 5 years (e.g., 5G deployment) while the service revenues have declined somewhat over the same period. This increased the Mobile Capex to Service Revenue ratio (note: based on Total Revenue, the ratio would be somewhat smaller, by ca. 17%). Source: Total Capex, Fixed, and Mobile Service revenues from New Street Research data for Western Europe. Note: The decomposition of the total Capex into Fixed and Mobile Capex is based on the author’s own statistical analysis and modeling. It is not a delivery of the New Street Research report.

CAN MOBILE-TRAFFIC GROWTH CONTINUE TO BE ACCOMMODATED CAPEX-WISE?

In my opinion, there has been much panic in our industry in the past about exhausting the cellular capacity of mobile networks and the imminent doom of our industry. A fear fueled by the exponential growth of user demand perceived inadequate spectrum amount and low spectral efficiency of the deployed cellular technologies, e.g., 3G-HSPA, classical passive single-in single-out antennas. Going back to the “hey-days” of 3G-HSPA, there was a fear that if cellular demand kept its growth rate, it would result in supply requirements going towards infinity and the required Capex likewise. So clearly an unsustainable business model for the mobile industry. Today, there is (in my opinion) no basis for such fears short or medium-term. With the increased fiberization of our society, where most homes will be connected to fiber within the next 5 – 10 years, cellular doomsday, in the sense of running out of capacity or needing infinite levels of Capex to sustain cellular demand, maybe a day never to come.

In Western Europe, the total mobile subscriber penetration was ca. 130% of the total population in 2021, with an excess of approximately 2.1+ mobile devices per subscriber. Mobile internet penetration was 76% of the total population in 2021 and is expected to reach 83% by 2025. In 2021, Europe’s average smartphone penetration rate was 77.6%, and it is projected to be around 84% by 2025. Also, by 2024±1, 50% of all connections in Western Europe are projected to be 5G connections. There are some expectations that around 2030, 6G might start being introduced in Western European markets. 2G and 3G will be increasingly phased out of the Western European mobile networks, and the spectrum will be repurposed for 4G and eventually 5G.

The above Figure 30 shows forecasted mobile users by their main mobile access technology. Source: based on the author’s forecast model relying on past technology diffusion trends for Western Europe and benchmarked against some WEU markets and other telco projections. See also 5G Standalone – European Demand & Expectations by Kim Larsen.

We may not see a complete phase-out of either older Gs, as observed in Figure 19. Due to a relatively large base of non-VOLTE (Voice-over-LTE) devices, mobile networks will have to support voice circuit-switched fallback to 2G or 3G. Furthermore, for the foreseeable future, it would be unlikely that all visiting roaming customers would have VOLTE-based devices. Furthermore, there might be legacy machine-2-machine businesses that would be prohibitively costly and complex to migrate from existing 2G or 3G networks to either LTE or 5G. All in all, ensure that 2G and 3G may remain with us for reasonably long.

Figure 31 above shows that mobile and fixed data traffic consumption is growing in totality and per-user level. On average mobile traffic grew faster than fixed from 2015 to 2021. A trend that is expected to continue with the introduction of 5G. Although the total traffic growth rate is slowing down somewhat over the period, on a per-user basis (mobile as well as fixed), the consumptive growth rate has remained stable.

Since the early days of 3G-HSPA (High-Speed Packet Access) radio access, investors and telco businesses have been worried that there would be an end to how much demand could be supported in our cellular networks. The “fear” is often triggered by seeing the exponential growth trend of total traffic or of the usage per customer (to be honest, that fear has not been made smaller by technology folks “panicking” as well).

Let us look at the numbers for 2021 as they are reported in the Cisco VNI report. The total mobile data traffic was in the order of 4 Exabytes (4 Billion gigabytes, GB), more than 5.5× the level of 2016. It is more than 600 million times the average mobile data consumption of 6.5 GB per month per customer (in 2021). Compare this with the Western European population of ca. 200 million. While big numbers, the 6.5 GB per month per customer is insignificant. Assuming that most of this volume comes from video streaming at an optimum speed of 3 – 5 Mbps (good enough for HD video stream), the 6.5 GB translates into approx. 3 – 5 hours of video streaming over a month.

The above Figure 32 Illustrates a 24-hour workday total data demand on the mobile network infrastructure. A weekend profile would be more flattish. We spend at least 12 hours in our home, ca. 7 hours at work (including school), and a maximum of 5 hours (~20%) commuting, shopping, and otherwise being away from our home or workplace. Previous studies of mobile traffic load have shown that 80% of a consumer’s mobile demand falls in 3 main radio node sites around the home and workplace. The remaining 20% tends to be much more mobile-like in the sense of being spread out over many different radio-node sites.

Daily we have an average of ca. 215 Megabytes per day (if spread equally over the month), corresponding to 6 – 10 minutes of video streaming. The average length of a YouTube was ca. 4.4 minutes. In Western Europe, consumers spend an average of 2.4 hours per day on the internet with their smartphones (having younger children, I am surprised it is not more than that). However, these 2.4 hours are not necessarily network-active in the sense of continuously demanding network resources. In fact, most consumers will be active somewhere between 8:00 to around 22:00, after which network demand reduces sharply. Thus, we have 14 hours of user busy time, and within this time, a Western European consumer would spend 2.4 hours cumulated over the day (or ca. 17% of the active time).

Figure 33 above illustrates (based on actual observed trends) how 5 million mobile users distribute across a mobile network of 5,000 sites (or radio nodes) and 15,000 sectors (typically 3 sectors = 1 site). Typically, user and traffic distributions tend to be log-norm-like with long tails. In the example above, we have in the busy hour a median value of ca. 80 users attached to a sector, with 15 being active (i.e., loading the network) in the busy hour, demanding a maximum of ca. 5 GB (per sector) or an average of ca. 330 MB per active user in the radio sector over that sector’s relevant busy hour.

Typically, 2 limits, with a high degree of inter-dependency, would allegedly hit the cellular businesses rendering profitable growth difficult at some point in the future. The first limit is a practical technology limit on how much capacity a radio access system can supply. As we will see a bit later, this will depend on the operator’s frequency spectrum position (deployed, not what might be on the shelf), the number of sites (site density), the installed antenna technology, and its effective spectral efficiency. The second (inter-dependent) limit is an economic limit. The incremental Capex that telcos would need to commit to sustaining the demand at a given quality level would become highly unprofitable, rendering further cellular business uneconomical.

From a Capex perspective, the cellular access part drives a considerable amount of the mobile investment demand. Together with the supporting transport, such as fronthaul, backhaul, aggregation, and core transport, the capital investment share is typically 50% or higher. This is without including the spectrum frequencies required to offer the cellular service. Such are usually acquired by local frequency spectrum auctions and amount to substantial investment levels.

In the following, the focus will be on cellular access.

The Cellular Demand.

Before discussing the cellular supply side of things, let us first explore the demand side from the view of a helicopter. Demand is created by users (N) of the cellular services offered by telcos. Users can be human or non-human such as things in general or more specific machines. Each user has a particular demand that, in an aggregated way, can be represented by the average demand in Bytes per User (d). Thus, we can then identify two growth drivers. One from adding new users (ΔN) to our cellular network and another from the incremental change in demand per user (ΔN) as time goes by.

It should be noted that the incremental change in demand or users might not per se be a net increase. Still, it could also be a net decrease, either because the cellular networks have reached the maximum possible level of capacity (or quality) that results in users either reducing their demand or “ churning” from those networks or that an alternative to today’s commercial cellular network triggers abandonment as high-demand users migrate to that alternative — leading both to a reduction in cellular users and the average demand per user. For example, a near-100% Fiber-to-the-Home coverage with supporting WiFi could be a reason for users to abandon cellular networks, at least in an indoor environment, which would reduce between 60 to 80% of present-day cellular data demand. This last (hypothetical) is not an issue for today’s cellular networks and telco businesses.

Of course, this can easily be broken down into many more drivers and details, e.g., technology diffusion or adaptation, the rate of users moving from one access technology to another (e.g., 3G→4G, 4G→5G, 5G→FTTH+WiFi), improved network & user device capabilities (better coverage, higher speeds, lower latency, bigger display size, device chip generation), new cellular service adaptation (e.g., TV streaming, VR, AR, …), etc.…

However, what is often forgotten is that the data volume of consumptive demand (in Byte) is not the main direct driver for network demand and, thus, not for the required investment level. A gross volumetric demand can be caused by various gross throughput demands (bits per second). The throughput demanded in the busiest hour ( or ) is the direct driver of network load, and thus, network investments, the volumetric demand, is a manifestation of that throughput demand.

With being the number of active users in a given radio cell at the time-instant of unit t taken within a day.  is the Bytes consumed in a time instant (e.g., typically a second); thus, 8  gives us the bits per time unit (or bits/sec), which is throughput consumed. Sum over all the cells’ instant throughput ( bits/sec) in the same instant and take the maximum across. For example, a day provides the busiest hour throughput for the whole network. Each radio cell drives its capacity provision and supply (in bits/sec) and the investments required to provide that demanded capacity in the air interface and front- and back-haul.

For example, if n = 6 active (concurrent) users, each consuming on average  = 0.625 Mega Bytes per second (5 Megabits per second, Mbps), the typical requirement for a YouTube stream with an HD 1080p resolution, our radio access network in that cell would experience a demanded load of 30 Mbps (i.e., 6×5 Mbps). Of course, provided that the given cell has sufficient capacity to deliver what is demanded. A 4G cellular system, without any special antenna technology, e.g., Single-in-Single-out (SiSo) classical antenna and not the more modern Multiple-in-Multiple-out (MiMo) antenna, can be expected to deliver ca. 1.5 Mbps/MHz per cell. Thus, we would need at least 20 MHz spectrum to provide for 6 concurrent users, each demanding 5 Mbps. With a simple 2T2R MiMo antenna system, we could support about 8 simultaneous users under the same conditions. A 33% increase in what our system can handle without such an antenna. As mobile operators implement increasingly sophisticated antenna systems (i.e., higher-order MiMo systems) and move to 5G, a leapfrog in the handling capacity and quality will occur.

Figure 34 Is the sky the limit to demand? Ultimately, the limit will come from the practical and economic limits to how much can be supplied at the cellular level (e.g., spectral bandwidth, antenna technology, and software features …). Quality will reduce as the supply limit is reached, resulting in demand adaptation, hopefully settling at a demand-supply (metastable) equilibrium.

Cellular planners have many heuristics to work with that together trigger when a given radio cell would be required to be expanded to provide more capacity, which can be provided by software (licenses), hardware (expansion/replacement), civil works (sectorization/cell splits) and geographical (cell split) means. Going northbound, up from the edge of the radio network up through the transmission chain, such as fronthaul, back, aggregation, and core transport network, may require additional investments in expanding the supplied demand at a given load level.

As discussed, mobile access and transport together can easily make up more than half of a mobile capital budget’s planned and budgeted Capex.

So, to know whether the demand triggers new expansions and thus capital demand as well as the resulting operational expenses (Opex), we really need to look at the supply side. That is what our current mobile network can offer. When it cannot provide a targeted level of quality, how much capacity do we have to add to the network to be on a given level of service quality?

The Cellular Supply.

Cellular capacity in units of throughput () given in bits per second, the basic building block of quality, is relatively easy to estimate. The cellular throughput (per unit cell) is provided by the amount of committed frequency spectrum to the air interface, what your radio access network and antenna support are, multiplied by the so-called spectral efficiency in bits per Hz per cell. The spectral efficiency depends on the antenna technology and the underlying software implementation of signal processing schemes enabling the details of receiving and sending signals over the air interface.

can be written as follows;

With Mbps being megabits (a million bits) per second and MHz being Mega Herz.

For example, if we have a site that covers 3 cells (or sectors) with a deployed 100 MHz @ 3.6GHz (B) on a 32T32R advanced antenna system (AAS) with an effective downlink (i.e., from the antenna to user), spectral efficiency of ca. 20 Mbps/MHz/cell (i.e., ), we should expect to have a cell throughput on average at 1,000 Mbps (1 Gbps).

The capacity supply formula can be applied to the cell-level consideration providing sizing and thus investment guidance as we move northbound up the mobile network and traffic aggregates and concentrates towards the core and connections points to the external internet.

From the demand planning (e.g., number of customers, types of services sold, etc..), that would typically come from the Marketing and Sales department within the telco company, the technical team can translate those plans into a network demand and then calculate what they would need to do to cope with the customer demand within an agreed level of quality.

In Figure 35 above, operators provide cellular capacity by deploying their spectral assets on an appropriate antenna type and system-level radio access network hardware and software. Competition can arise from a superior spectrum position (balanced across low, medium, and high-frequency bands), better or more aggressive antenna technology, and utilizing their radio access supplier(s)’ features (e.g., signal processing schemes). Usually, the least economical option will be densifying the operator’s site grid where needed (on a macro or micro level).

Figure 36 above shows the various options available to the operator to create more capacity and quality. In terms of competitive edge, more spectrum than competitors provided it is being used and is balanced across low, medium, and high bands, provides the surest path to becoming the best network in a given market and is difficult to economically copy by operators with substantially less spectrum. Their options would be compensating for the spectrum deficit by building more sites and deploying more aggressive antenna technologies. The last one is relatively easy to follow by anyone and may only provide some respite temporarily.  

An average mobile network in Western Europe has ca. 270 MHz spectrum (60 MHz low-band below 1800 and 210 MHz medium-band below 5 GHz) distributed over an average of 7 cellular frequency bands. It is rare to see all bands deployed in actual deployments and not uniformly across a complete network. The amount of spectrum deployed should match demand density; thus, more spectrum is typically deployed in urban areas than in rural ones. In demand-first-driven strategies, the frequency bands will be deployed based on actual demand that would typically not require all bands to be deployed. This is opposed to MNOs that focus on high quality, where demand is less important, and where typically, most bands would be deployed extensively across their networks. The demand-first-driven strategy tends to be the most economically efficient strategy as long as the resulting cellular quality is market-competitive and customers are sufficiently satisfied.

In terms of downlink spectral capacity, we have an average of 155 MHz or 63 MHz, excluding the C-band contribution. Overall, this allows for a downlink supply of a minimum of 40 GB per hour (assuming low effective spectral efficiency, little advanced antenna technology deployed, and not all medium-band being utilized, e.g., C-Band and 2.5 GHz). Out of the 210 MHz mid-band spectrum, 92 MHz falls in the 3.X GHz (C-band) range and is thus still very much in the process of being deployed for 5G (as of June 2022). The C-band has, on average, increased the spectral capacity of Western European telcos by 50+% and, with its very high suitability for deployment together with massive MiMo and advanced antenna systems, effectively more than doubled the total cellular capacity and quality compared to pre-C-band deployment (using a 64T64R massive MiMo as a reference with today’s effective spectral efficiency … it will be even better as time goes by).

Figure 37 (above) shows the latest Ookla and OpenSignal DL speed benchmarks for Western Europe MNOs (light blue circles), and comparing this with their spectrum holdings below 3.x GHz indicates that there may be a lot of unexploited cellular capacity and quality to be unleashed in the future. Although, it would not be for free and likely require substantial additional Capex if deemed necessary. The ‘Expected DL Mbps’ (orange solid line, *) assumes the simplest antenna setup (e.g., classical SiSo antennas) and that all bands are fully used. On average, MNOs above the benchmark line have more advanced antenna setups (higher-order antennas) and fully (or close to) spectrum deployment. MNOs below the benchmark line likely have spectrum assets that have not been fully deployed yet and (or) “under-prioritized” their antenna technology infrastructure. The DL spectrum holding excludes C- and mmWave spectrum. Note:  There was a mistake in the original chart published on LinkedIn as the data was depicted against the total spectrum holding (DL+UL) and not only DL. Data: 54 Western European telcos.

Figure 37 illustrates the Western European cellular performance across MNOs, as measured by DL speed in Mbps, and compares this with the theoretical estimate of the performance they could have if all DL spectrum (not considering C-band, 3.x GHz) in their portfolio had been deployed at a fairly simple antenna setup (mainly SiSo and some 2T2R MiMo) with an effective spectral efficiency of 0.85 Mbps per MHz. It is good to point out that this is expected of 3G HSPA without MiMo. We observe that 21 telcos are above the solid (orange) line, and 33 have an actual average measured performance that is substantially below the line in many cases. Being above the line indicates that most spectrum has been deployed consistently across the network, and more advanced antennas, e.g., higher-order MiMo, are in use. Being below the line does (of course) not mean that networks are badly planned or not appropriately optimized. Not at all. Choices are always made in designing a cellular network. Often dictated by the economic reality of a given operator, geographical demand distribution, clutter particularities, or the modernization cycle an operator may be in. The most obvious reasons for why some networks are operating well under the solid line are; (1) Not all spectrum is being used everywhere (less in rural and more in urban clutter), (2) Rural configurations are simpler and thus provide less performance than urban sites. We have (in general) more traffic demand in urban areas than in rural. Unless a rural area turns seasonally touristic, e.g., lake Balaton in Hungary in the summer … It is simply a good technology planning methodology to prioritize demand in Capex planning, and it makes very good economic sense (3) Many incumbent mobile networks have a fundamental grid based on (GSM) 900MHz and later in-filled for (UMTS) 2100MHz…which typically would have less site density than networks based on (DCS) 1800MHz. However, site density differences between competing networks have been increasingly leveled out and are no longer a big issue in Western Europe (at least).

Overall, I see this as excellent news. For most mobile operators, the spectrum portfolio and the available spectrum bandwidth are not limiting factors in coping with demanded capacity and quality. Operators have many network & technology levers to work with to increase both quality and capacity for their customers. Of course, subject to a willingness to prioritize their Capex accordingly.

A mobile operator has few options to supply cellular capacity and quality demanded by its customer base.

  • Acquire more spectrum bandwidth by buying in an auction, buying from 3rd party (including M&A), asymmetric sharing, leasing, or trading (if regulatory permissible).
  • Deploy a better (spectral efficient) radio access technology, e.g., (2G, 3G) → (4G, 5G) or/and 4G → 5G, etc. Benefits will only be seen once a critical mass of customer terminal equipment supporting that new technology has been reached on the network (e.g., ≥20%).
  • Upgrade antenna technology infrastructure from lower-order passive antennas to higher-order active antenna systems. In the same category would be to ensure that smart, efficient signal processing schemes are being used on the air interface.
  • Building a denser cellular network where capacity demand dictates or coverage does not support the optimum use of higher frequency bands (e.g., 3.x GHz or higher).
  • Small cell deployment in areas where macro-cellular built-out is no longer possible or prohibitively costly. Though small cells scale poorly with respect to economics and maybe really the last resort.

Sectorization with higher-frequency massive-MiMo may be an alternative to small-cell and macro-cellular additions. However, sectorization requires that it is possible civil-engineering wise (e.g., construction) re: structural stability, permissible by the landlord/towerco and finally economic compared to a new site built. Adding more than the usual 3-sectors to a site would further boost site spectral efficiency as more antennas are added.

Acquiring more spectrum requires that such spectrum is available either by a regulatory offering (public auction, public beauty contest) or via alternative means such as 3rd party trading, leasing, asymmetric sharing, or by acquiring an MNO (in the market) with spectrum. In Western Europe, the average cost of spectrum is in the ballpark of 100 million Euro per 10 million population per 20 MHz low-band or 100 MHz medium bands. Within the European Union, recent auctions provide a 20-year usage-rights period before the spectrum would have to be re-auctioned. This policy is very different from, for example, in the USA, where spectrum rights are bought and ownership secured in perpetuity (sometimes conditioned on certain conditions being met). For Western Europe, apart from the mmWave spectrum, in the foreseeable future, there will not be many new spectrum acquisition opportunities in the public domain.

This leaves mobile operators with other options listed above. Re-farming spectrum away from legacy technology (e.g., 2G or 3G) in support of another more spectral efficient access technology (e.g., 4G and 5G) is possibly the most straightforward choice. In general, it is the least costly choice provided that more modern options can support the very few customers left. For either retiring 2G or 3G, operators need to be aware that as long as not all terminal equipment support Voice-over-LTE (VoLTE), they need to keep either 2G or 3G (but not both) for 4G circuit-switched fallback (to 2G or 3G) for legacy voice services. The technologist should be prepared for substantial pushback from the retail and wholesale business, as closing down a legacy technology may lead to significant churn in that legacy customer base. Although, in absolute terms, the churn exposure should be much smaller than the overall customer base. Otherwise, it will not make sense to retire the legacy technology in the first place. Suppose the spectral re-farming is towards a new technology (e.g., 5G). In that case, immediate benefits may not occur before a critical mass of capable devices is making use of the re-farmed spectrum. The Capex impact of spectral re-farming tends to be minor, with possibly some licensing costs associated with net savings from retiring the legacy. Most radio departments within mobile operators, supplier experts, and managed service providers have gained much experience in this area over the last 5 – 7 years.

Another venue that should be taken is upgrading or modernizing the radio access network with more capable antenna infrastructure, such as higher-order massive MiMo antenna systems. As has been pointed out by Prof. Emil Björnson also, the available signal processing schemes (e.g., for channel estimation, pre-coding, and combining) will be essential for the ultimate gain that can be achieved. This will result in a leapfrog increase in spectral efficiency. Thus, directly boosting air-interface capacity and the quality that the mobile customer can enjoy. If we take a 20-year period, this activity is likely to result in a capital demand in the order of 100 million euros for every 1,000 sites being modernized and assumes a modernization (or obsolescence) cycle of 7 years. In other words, within the next 20 years, a mobile operator will have undergone at least 3 antenna-system modernization cycles. It is important to emphasize that this does not (entirely) cover the likely introduction of 6G during the 20 years. Operators face two main risks in their investment strategy. One risk is that they take a short-term look at their capital investments and customer demand projections. As a result, they may invest in insufficient infrastructure solutions to meet future demands, forcing accelerated write-offs and re-investments. The second significant risk is that the operator invests too aggressively upfront in what appears to be the best solution today to find substantially better and more efficient solutions in the near future that more cautious competitive operators could deploy and achieve a substantially higher quality and investment efficiency. Given the lack of technology maturity and the very high pace of innovation in advanced antenna systems, the right timing is crucial but not straightforward.

Last and maybe least, the operator can choose to densify its cellular grid by adding one or more macro-cellular sites or adding small cells across existing macro-cellular coverage. Before it is possible to build a new site or site, the operator or the serving towerco would need to identify suitable locations and subsequently obtain a permit to establish the new site or site. In urban areas, which typically have the highest macro-site densities, getting a new permit may be very time-consuming and with a relatively high likelihood of not being granted by the municipality. Small cells may be easier to deploy in urban environments than in macro sites. For operators making use of towerco to provide the passive site infrastructure, the cost of permitting and building the site and materials (e.g., steel and concrete) is a recurring operational expense rather than a Capex charge. Of course, active equipment remains a Capex item for the relevant mobile operator.

The conclusion I make above is largely consistent with the conclusions made by New Street Research in their piece “European 5G deep-dive” (July 2021). There is plenty of unexploited spectrum with the European operators and even more opportunity to migrate to more capable antenna systems, such as massive-MiMo and active advanced antenna systems. There are also above 3GHz, other spectrum opportunities without having to think about millimeter Wave spectrum and 5G deployment in the high-frequency spectrum range.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. There should be no doubt that without the support of Russell Waller (New Street Research), this blog would not have been possible. Thank you so much for providing much of the data that lays the ground for much of the Capex analysis in this article. Of course, a lot of thanks go out to my former Technology and Network Economics colleagues, who have been a source of inspiration and knowledge. I cannot get away with acknowledging Maurice Ketel (who for many years let my Technology Economics Unit in Deutsche Telekom, I respect him above and beyond), Paul Borker, David Haszeldine, Remek Prokopiak, Michael Dueser, Gudrun Bobzin, as well as many, many other industry colleagues who have contributed with valuable insights, discussions & comments throughout the years. Many thanks to Paul Zwaan for a lot of inspiration, insights, and discussions around IT Architecture.

Without executive leadership’s belief in the importance of high-quality techno-financial models, I have no doubt that I would not have been able to build up the experience I have in this field. I am forever thankful, for the trust and for making my professional life super interesting and not just a little fun, to Mads Rasmussen, Bruno Jacobfeuerborn, Hamid Akhavan, Jim Burke, Joachim Horn, and last but certainly not least, Thorsten Langheim.

FURTHER READING.

  1. Kim Kyllesbech Larsen, “The Nature of Telecom Capex.” (July, 2022). My first article laying the ground for Capex in the Telecom industry. The data presented in this article is largely outdated and remains for comparative reasons.
  2. Kim Kyllesbech Larsen, “5G Standalone European Demand Expectations (Part I).”, (January, 2022).
  3. Kim Kyllesbech Larsen, “RAN Unleashed … Strategies for being the best (or the worst) cellular network (Part III).”, (January, 2022).
  4. Tom Copeland, Tim Koller, and Jack Murrin, “Valuation”, John Wiley & Sons, (2000). I regard this as my “bible” when it comes to understanding enterprise valuation. There are obviously many finance books on valuation (I have 10 on my bookshelf). Copeland’s book is the best imo.
  5. Stefan Rommer, Peter Hedman, Magnus Olsson, Lars Frid, Shabnam Sultana, and Catherine Mulligan, “5G Core Networks”, Academic Press, (2020, 1st edition). Good account for what a 5G Core Network entails.
  6. Jia Shen, Zhongda Du, Zhi Zhang, Ning Yang and Hai Tang, “5G NR and enhancements”, Elsevier (2022, 1st edition). Very good and solid account of what 5G New Radio (NR) is about and the considerations around it.
  7. Wim Rouwet, “Open Radio Access Network (O-RAN) Systems Architecture and Design”, Academic Press, (2022). One of the best books on Open Radio Access Network architecture and design (honestly, there are not that many books on this topic yet). I like that the author, at least as an introduction makes the material reasonably accessible to even non-experts (which tbh is also badly needed).
  8. Strand Consult, “OpenRAN and Security: A Literature Review”, (June, 2022). Excellent insights into the O-RAN maturity challenges. This report focuses on the many issues around open source software-based development that is a major part of O-RAN and some deep concerns around what that may mean for security if what should be regarded as critical infrastructure. I warmly recommend their “Debunking 25 Myths of OpenRAN”.
  9. Ian Morris, “Open RAN’s 5G course correction takes it into choppy waters”, Light Reading, (July, 2023).
  10. Hwaiyu Geng P.E., “Data Center Handbook”, Wiley (2021, 2nd edition). I have several older books on the topic that I have used for my models. This one brings the topic of data center design up to date. Also includes the topic of Cloud and Edge computing. Good part on Data Center financial analysis. 
  11. James Farmer, Brian Lane, Kevin Bourgm Weyl Wang, “FTTx Networks, Technology Implementation, and Operations”, Elsevier, (2017, 1st edition). It has some books covering FTTx deployment, GPON, and other alternative fiber technologies. I like this one in particular as it covers hands-on topics as well as basic technology foundations.
  12. Tower companies overview, “Top-12 Global 5G Cell Tower Companies 2021”, (Nov. 2021). A good overview of international tower companies with a meaningful footprint in Europe.
  13. New Street Research, “European 5G deep-dive”, (July, 2021).
  14. Prof. Emil Björnson, https://ebjornson.com/research/ and references therein. Please take a look at many of Prof. Björnson video presentations (e.g., many brilliant YouTube presentations that are fairly assessable).

Spectrum in the USA – An overview of Today and a new Tomorrow.

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This week (Week 17, 2023), I submitted my comments and advice titled “Development of a National Spectrum Strategy (NSS)” to the United States National Telecommunications & Information Administration (NTIA) related to their work on a new National Spectrum Strategy.

Of course, one might ask why, as a European, bother with the spectrum policy of the United States. So hereby, a bit of reasoning for bothering with this super interesting and challenging topic of spectrum policy on the other side of the pond.

A EUROPEAN IN AMERICA.

As a European coming to America (i.e., USA) for the first time to discuss the electromagnetic spectrum of the kind mobile operators love to have exclusive access to, you quickly realize that Europe’s spectrum policy/policies, whether you like them or not, are easier to work with and understand. Regarding spectrum policy, whatever you know from Europe is not likely to be the same in the USA (though physics is still fairly similar).

I was very fortunate to arrive back in the early years of the third millennium to discuss cellular capacity and, as it quickly evolves (“escalates”), too, having a discussion of available cellular frequencies, the associated spectral bandwidth, and whether they really need that 100 million US dollar for radio access expansions.

Why fortunate?

I was one of the first (from my company) to ask all those “stupid” questions whenever I erroneously did not just assume things surely must be the same as in Europe and ended up with the correct answer that in the USA, things are a “little” different and a lot more complicated in terms of the availability of frequencies and what feeds the demand … the spectrum bandwidth. My arrival was followed by “hordes” of other well-meaning Europeans with the same questions and presumptions, using European logic to solve US challenges. And that doesn’t really work (surprised you not should be). I believe my T-Mobile US colleagues and friends over the years surely must have felt like Groundhog Day all over again at every new European visit.

COMPARING APPLES AND ORANGES.

Looking at US spectrum reporting, it is important to note that it is customary to provide the total amount of spectrum. Thus, for FDD spectrum bands, including both the downlink spectrum portion and uplink spectrum part of the cellular frequency band in question. For example, when a mobile network operator (MNO) reports that it has, e.g., 40 MHz of AWS1 spectrum in San Diego (California), it means that it has 2×20 MHz (or 20+20 MHz). Thus, 20 MHz of downlink (DL) services and 20 MHz of uplink (UL) services. For FDD, both the DL and the UL parts are counted. In Europe, historically, we mainly would talk about half the spectrum for FDD spectrum bands. This is one of the first hurdles to get over in meetings and discussions. If not sorted out early can lead to some pretty big misunderstandings (to say the least). To be honest, and in my opinion, providing the full spectrum holding, irrespective of whether a band is used as FDD or TDD, is less ambiguous than the European tradition.

The second “hurdle” is to understand that a USA-based MNO is likely to have a substantial variation in its spectrum holdings across the US geography. An MNO may have a 40 MHz (i.e., 2×20 MHz) PCS spectrum in Los Angeles (California) and only 30 MHz (2×15 MHz) of the same spectrum in New York or only 20 MHz (2×10 MHz) in Miami (Florida). For example, FCC (i.e., the regulator managing non-federal spectrum) uses 734 so-called Cellular Market Areas or CMAs, and there is no guarantee that a mobile operator’s spectrum position will remain the same over these 734 CMAs. Imagine Dutch (or other European) mobile operators having a varying 700 MHz (used for 5G) spectrum position across the 342 municipalities of The Netherlands (or another European country). It takes a lot of imagination … right? And maybe why, we Europeans, shake our heads at the US spectrum fragmentation, or market variation, as opposed to our nice, neat, and tidy market-wise spectrum uniformity. But is the European model so much better (apart from being neat & tidy)? …

… One may argue that the US model allows for spectrum acquisition to be more closely aligned with demand, e.g., less spectrum is needed in low-population density areas and more is required in high-density population areas (where demand will be much more intense). As evidenced by many US auctions, the economics matched the demand fairly well. While the European model is closely aligned with our good traditions of being solid on average … with our feet in the oven and our head in the freezer … and on average all is pretty much okay in Europe.

Figure 1 and 2 below illustrates a mobile operator difference between its spectrum bandwidth spread across the 734 US-defined CMAs in the AWS1 band and how that would look in Europe.

Figure 1 illustrates the average MNO distribution of (left chart) USA AWS1 band (band 4) distribution over the 734 Cellular Market Areas (CMA) defined by the FCC. (right chart) Typical European 3 MNO 2100-band (band-1) distribution across the country’s geographical area. As a rule of thumb for European countries, the spectrum is fairly uniformly distributed across the national MNOs. E.g., if you have 3 mobile operators, the 120 MHz available to band-1 will be divided equally among the 3, and If there are 4 MNOs, then it will be divided by 4. Nevertheless, in Europe, an MNO spectrum position is fixed across the geography.

Figure 2 below is visually an even stronger illustration of mobile operator bandwidth variation across the 734 cellular market areas. The dashed white horizontal line is if the PCS band (a total of 120 MHz or 2×60 MHz) would be shared equally between 4 main nationwide mobile operators ending up at 30 MHz per operator across all CMAs. This would resemble what today is more or less a European situation, i.e., irrespective of regional population numbers, the mobile operator’s spectrum bandwidth at a given carrier frequency would be the same. The European model, of course, also implies that an operator can provide the same quality in peak bandwidth before load may become an issue. The high variation in the US operator’s spectrum bandwidth may result in a relatively big variation in provided quality (i.e., peak speed in Mbps) across the different CMAs.

There is an alternative approach to spectrum acquisition that may also be more spectrally efficient, which the US model is much more suitable for. Aim at a target Hz per Customer (i.e., spectral overhead) and keep this constant within the various market. Of course, there is a maximum realistic amount of bandwidth to acquire, governed by availability (e.g., for PCS, that is, 120 MHz) and competitive bidders’ strength. There will also be a minimum bandwidth level determined by the auction rules (e.g., 5 MHz) and a minimum acceptable quality level (e.g., 10 MHz). However, Figure 2 below reflects more opportunistic spectrum acquisition in CMAs with less than a million population as opposed to a more intelligent design (possibly reflecting the importance of, or lack of, different CMAs to the individual operators).

Figure 2 illustrates the bandwidth variation (orange dots) across the 734 cellular market areas for 4 nationwide mobile network operators in the United States. The horizontal dashed white line is if the four main nationwide operators would equally share the 120 MHz of PCS spectrum (fairly similar to a European situation). MNOs would have the same spectral bandwidth across every CMA. The Minimum – Growing – Maximum dashed line illustrates a different spectrum acquisition strategy, where the operator has fixed the amount of spectrum per customer required and keeps this as a planning rule between a minimum level (e.g., a unit of minimum auctioned bandwidth) and a realistic maximum level (e.g., determined by auction competition, auction ruling, and availability).

Thirdly, so-called exclusive use frequency licenses (as opposed to shared frequencies), as issued by FCC, can be regarded accounting-wise as an indefinitely-lived intangible asset. Thus, once a US-based cellular mobile operator has acquired a given exclusive-use license, that license can be considered disposable to the operator in perpetuity. It should be noted that FCC licenses typically would be issued for a fixed (limited) period, but renewals are routine.

This is a (really) big difference from European cellular frequency licenses that typically expire after 10 – 20 years, with the expired frequency bands being re-auctioned. A European mobile operator cannot guarantee its operation beyond the expiration date of the spectrum acquired, posing substantial existential threats to business and shareholder value. In the USA, cellular mobile operators have a substantially lower risk regarding business continuity as their spectrum, in general, can be regarded as theirs indefinitely.

FCC also operates with a shared-spectrum license model, as envisioned by the Citizens Broadband Radio Service (CBRS) in the 3.55 to 3.7 GHz frequency range (i.e., the C-band). A shared-spectrum license model allows for several types of users (e.g., Federal and non-Federal) and use-cases (e.g., satellite communications, radar applications, national cellular services, local community broadband services, etc..) to co-exist within the same spectrum band. Usually, such shared licenses come with firm protection of federal (incumbent) users that allows commercial use to co-exist with federal use, though with the federal use case taking priority over the non-federal. A really good overview of the CBRS concept can be found in “A Survey on Citizens Broadband Radio Service (CBRS)” by P. Agarwal et al.. Wireless Innovation Forum published on 2022 a piece on “Lessons Learned from CBRS” which provides a fairly nuanced, although somewhat negative, view on spectrum sharing as observed in the field and within the premises of the CBRS priority architecture and management system.

Recent data around FCC’s 3.5 GHz (CBRS) Auction 105 would indicate that shared-licensed spectrum is valued at a lower USD-per-MHz-pop (i.e., 0.14 USD-per-MHz-pop) than exclusive-use license auctions in 3.7 GHz (Auction 107; 0.88 USD-per-MHz-pop) and 3.45 GHz (Auction 110; 0.68 USD-per-MHz-pop). The duration of the shared-spectrum license in the case of the Auction 105 spectrum is 10 years after which it is renewed. Verizon and Dish Networks were the two main telecom incumbents that acquired substantial spectrum in Auction 105. AT&T did not acquire and T-Mobile US only picked close to nothing (i.e., 8 licenses).

THE STATE OF CELLULAR PERFORMANCE – IN THE UNITED STATES AND THE REST OF THE WORLD.

Irrespective of how one feels about the many mobile cellular benchmarks around in the industry (e.g., Ookla Speedtest, Umaut benchmarking, OpenSignal, etc…), these benchmarks do give an indication of the state of networks and how those networks utilize the spectral resources that mobile companies have often spend hundreds of millions, if not billions, of US dollars acquiring and not to underestimate in cost and time, spectrum clearing or perfecting a “second-hand” spectrum may incur for those operators.

So how do US-based mobile operators perform in a global context? We can get an impression, although very 1-dimensional, from Figure 1 below.

Figure 3 illustrates the comparative results of Ookla Speedtest data in median downlink speed (Mbps) for various countries. The selection of countries provides a reasonable representation of maximum and minimum values. To give an impression of the global ranking as of February 2023; South Korea (3), Norway (4), China (7), Canada (17), USA (19), and Japan (48). As a reminder, the statistic is based on the median of all measurements per country. Thus, half of the measurements were above the median speed value, and the other half were below. Note: median values from 2020 to 2017 are estimated as Ookla did only provide average numbers.

Ookla’s Speedtest rank (see Figure 3 above) positions the United States cellular mobile networks (as an average) among the Top-20. Depending on the ambition level, that may be pretty okay or a disappointment. However, over the last 24 months, thanks to the fast 5G deployment pace at 600 MHz, 2.5 GHz, and C-band, the US has leapfrogged (on average) its network quality which for many years did not improve much due to little spectrum availability and huge capital investment levels. Something that the American consumer can greatly enjoy irrespective of the relative mobile network ranking of the US compared to the rest of the world. South Korea and Norway are ranked 3 and 4, respectively, regarding cellular downlink (DL) speed in Mbps. The above figure also shows a significant uplift in the speed at the time of introducing 5G in the cellular operators’ networks worldwide.

How to understand the supplied cellular network quality and capacity that the consumer demand and hopefully also enjoy? Let start with the basics:

Figure 4 illustrates one of the most important (imo) to understand about creating capacity & quality in cellular networks. You need frequency bandwidth (in MHz), the right technology boosting your spectral efficiency (i.e., the ability to deliver bits per unit Hz), and sites (sectors, cells, ..) to deploy the spectrum and your technology. That’s pretty much it.

We might be able to understand some of the dynamics of Figure 3 using Figure 4, which illustrates the fundamental cellular quality (and capacity) relationship with frequency bandwidth, spectral efficiency, and the number of cells (or sectors or sites) deployed in a given country.

Thus, a mobile operator can improve its cellular quality (and capacity) by deploying more spectrum acquired on its existing network, for example, by auctions, leasing, sharing, or other arrangements within the possibilities of whatever applicable regulatory regime. This option will exhaust as the operator’s frequency spectrum pool is deployed across the cellular network. It leaves an operator to wait for an upcoming new frequency auction or, if possible, attempt to purchase additional spectrum in the market (if regulation allows) that may ultimately include a merger with another spectrum-rich entity (e.g., AT&T attempt to take over T-Mobile US). All such spectrum initiatives may take a substantial amount of time to crystalize, while customers may experience a worsening in their quality. In Europe, the licensed spectrum becomes available in cycles of 10 – 20 years. In the USA, exclusive-use licensed spectrum typically would be a once-only opportunity to acquire (unless you acquire another spectrum-holding entity later, e.g., Metro PCS, Sprint, AT&T’s attempt to acquire T-Mobile, …).

Another part of the quality and capacity toolkit is for the mobile operator to choose appropriately spectral efficient technologies that are supported by a commercially available terminal ecosystem. Firstly, migrate frequency and bandwidth away from currently deployed legacy radio-access technology (e.g., 2G, 3G, …) to newer and spectrally more efficient ones (e.g., 4G, 5G, …). This migration, also called spectral re-farming, requires a balancing act between current legacy demand versus the future expectations of demand in the newer technology. In a modern cellular setting, the choice of antenna technology (e.g., massive MiMo, advanced antenna systems, …) and type (e.g., multi-band) is incredibly important for boosting quality and capacity within the operators’ cellular networks. Given that such choices may result in redesigning existing site infrastructure, it provides an opportunity to optimize the existing infrastructure for the best coverage of the consolidated spectrum pool. It is likely that the existing infra was designed with a single or only a few frequencies in mind (e.g., PCS, PCS+AWS, …) as well as legacy antennas, and the cellular performance is likely improved by considering the complete pool of frequencies in the operator’s spectrum holding. The mobile operator’s game should always be to achieve the best possible spectral efficiency considering demand and economics (i.e., deploying 64×64 massive MiMo all over a network may be the most spectrally efficient solution, theoretically, but both demand and economics would rarely support such an apparently “silly” non-engineering strategy). In general, this will be the most frequently used tool in the operators’ quality/capacity toolkit. I expect to see an “arms race” between operators deploying the best and most capable antennas (where it matters), as it will often be the only way to differentiate in quality and capacity (if everything else is almost equal).

Finally, the mobile operator can deploy more site locations (macro and small cells), if permitting allows, or more sectors by sectorization (e.g., 3 → 4, 4 → 5 sectors) or cell split if the infrastructure and landlord allows. If there remains unused spectral bandwidth in the operator’s spectrum pool, the operator may likely choose to add another cell (i.e., frequency band) to the existing site. Particular adding new site locations (macro or small cell) is the most complex path to be taken and, of course, also often the least economic path.

Thus, to get a feeling for the Ookla Speedtest, which is a country average, results of Figure 3, we need, as a starting point, to have the amount of spectral bandwidth for the average cellular mobile operator. This is summarised in below’s Table 1.

Table 1 provides, per country, the average amount of Low-band (≤ 1 GHz), Mid-band (1 GHz to 2.1 GHz), 2.3 & 2.5 GHz bands, Sub-total bandwidth before including the C-band, the C-band (3.45 to 4.2 GHz) and the Total bandwidth. The table also includes the Ookla Global Speedtest DL Mbps and Global Rank as of February 2023. I have also included the in-country mobile operator variation within the different categories, which may indicate what kind of performance range to expect within a given country.

It does not take too long to observe that there is only an apparently rather weak correlation between spectrum bandwidth (sub-total and total) and the observed DL speed (even after rescaling to downlink spectrum only). Also, what is important is, of course, how much of the spectrum is deployed. Typically low and medium bands will be deployed extensively, while other high-frequency bands may only have been selectively deployed, and the C-band is only in the process of being deployed (where it is available). What also plays a role is to what degree 5G has been rollout across the network, how much bandwidth has been dedicated to 5G (and 4G), and what type of advanced antenna system or massive MiMo capabilities has been chosen. And then, to provide a great service, a network must have a certain site density (or coverage) compared to the customer’s demand. Thus, it is to be expected that the number of mobile site locations, and the associated number of frequency cells and sectors, will play a role in the average speed performance of a given country.

Figure 5 illustrates how the DL speed in Mbps correlates with the (a) total amount of spectrum excluding the C-band (still not widely deployed), (b) Customers per Site that provides a measure of the customer load at the site location level. The more customers load a site or compete for radio resources (i.e., MHz), the lower the experience. Finally, (c) The higher the Site times, the bandwidth is compared to the number of customers. More quality can be provided (as observed with the positive correlation). The data is from Table 1.

Figure 5 shows that load (e.g., customers per site) and available capacity (e.g., sites x bandwidth) relative to customers are strongly correlated with the experienced quality (e.g., speed in Mbps). The comparison between the United States and China is interesting as both countries with a fairly similar surface area (i.e., 9.8 vs. 9.6 million sq. km), the USA has a little less than a quarter of the population, and the average mobile US operator would have about one-third of the customers compared to the average Chinese operator (note: China mobile dominates the average). The Chinese operator, ignoring C-band, would have ca. 25 MHz or ~+20% (~50 MHz or ca. +10% if C-band is included) more than the US operator. Regarding sites, China Mobile has been reported to have millions of cell site locations (incl. lots of small cells). The US operator’s site count is in the order of hundreds of thousands (though less than 200k currently, including small cells). Thus, Chinese mobile operators have between 5x to 10x the number of site locations compared to the American ones. While the difference in spectrum bandwidth has some significance (i.e., China +10% to 20% higher), the huge relative difference in site numbers is one of the determining factors in why China (i.e., 117 Mbps) gets away with a better speed test score that is better than the American one (i.e., 85 Mbps). While theoretically (and simplistically), one would expect that the average Chinese mobile operator should be able to provide more than twice the speed as compared to the American mobile operator instead of “only” about 40% more, it stands to show that the radio environment is a “bit” more complex than the simplistic view.

Of course, the US-based operator could attempt to deploy even more sites where it matters. However, I very much doubt that this would be a feasible strategy given permitting and citizen resistance to increasing site density in areas where it actually would be needed to boost the performance and customer experience.

Thus, the operator in the United States must acquire more spectrum bandwidth and deploy that where it matters to their customers. They also need to continue to innovate on leapfrogging the spectral efficiency of the radio access technologies and deploy increasingly more sophisticated antenna systems across their coverage footprint.

In terms of sectorization (at existing locations), cell split (adding existing spectrum to an existing site), and/or adding more sophisticated antenna systems is a matter of Capex prioritization and possibly getting permission from the landlord. Acquiring new spectrum … well, that depends on such new spectrum somehow becomes available.

Where to “look” for more spectrum?

WHERE COULD MORE SPECTRUM COME FROM?

Within the so-called “beachfront spectrum” covering the frequency range from 225 MHz to 4.2 GHz (according to NTIA), only about 30% (ca. 1GHz of bandwidth within the frequency range from 600 MHz to 4.2 GHz) is exclusively non-Federal, and mainly with the mobile operators as exclusive use licenses deployed for cellular mobile services across the United States. Federal authorities exclusively use a bit less than 20% (~800 MHz) for communications, radars, and R&D purposes. This leaves ca. 50% (~2 GHz) of the beachfront spectrum shared between Federal authorities and commercial entities (i.e., non-Federal).

For cellular mobile operators, exclusive use licenses would be preferable (note: at least at the current state of the relevant technology landscape) as it provides the greatest degree of operational control and possibility to optimize spectral efficiency, avoiding unacceptable levels of interference either from systems or towards systems that may be sharing a given frequency range.

The options for re-purposing the Federal-only spectrum (~800 MHz) could, for example, be either (a) moving radar systems’ operational frequency range out of the beachfront spectrum range to the degree innovation and technology supports such a migration, (b) modernizing radar systems with a focus of making these substantially more spectrally efficient and interference-resistant, (c) migrated federal-only communications services to commercially available systems (e.g., 5G federal-only slicing) similar to the trend of migrating federal legacy data centers to the public cloud. Within the shared frequency portion with the ~2 GHz of bandwidth, it may be more challenging as considerable commercial interests (other than mobile operators) have positioned that business at and around such frequencies, e.g., within the CBRS frequency range. This said, there might also be opportunities within the Federal use cases to shift applications towards commercially available communication systems or to shift them out of the beachfront range. Of course, in my opinion, it always makes sense to impose (and possibly finance) stricter spectral efficiency conditions, triggering innovation on federal systems and commercial systems alike within the shared portion of the beachfront spectrum range. With such spectrum strategies, it appears compelling that there are high likelihood opportunities for creating more spectrum for exclusive license use that would safeguard future consumer and commercial demand and continuous improvement of customer experience that comes with the future demand and user expectations of the technology that serves them.

I believe that the beachfront should be extended beyond 4.2 GHz. For example aligning with band-79, whose frequency range extends from 4.4 GHz to 5 GHz, allows for a bandwidth of 600 MHz (e.g., China Mobile has 100 MHz in the range from 4.8 GHz to 4.9 GHz). Exploring additional re-purposing opportunities for exclusive use licenses in what may be called the extended beachfront frequency range from 4.2 GHz up to 7.2 GHz should be conducted with priority. Such a study should also consider the possibility of moving the spectrum under exclusive and shared federal use to other frequency bands and optimizing the current federal frequency and spectrum allocation.

The NTIA, that is, the National Telecommunications and Information Administration, is currently (i.e., 2023) for the United States developing a National Spectrum Strategy (NSS) and the associated implementation plan. Comments and suggestions to the NSS were possible until the 18th of April, 2023. The National Spectrum Strategy should address how to create a long-term spectrum pipeline. It is clear that developing a coherent national spectrum strategy is critical to innovation, economic competition, national security, and maybe re-capture global technology leadership.

So who is the NTIA? What do they do that FCC doesn’t already do? (you may possibly ask).

WHO MANAGES WHAT SPECTRUM?

Two main agencies in the US manage the frequency spectrum, the FCC and the NTIA.The Federal Communications Commission, the FCC for short, is an independent agency that exclusively regulates all non-Federal spectrum use across the United States. FCC allocates spectrum licenses for commercial use, typically through spectrum auctions. A new or re-purposed commercialized spectrum has been reclaimed from other uses, both from federal uses and existing commercial uses. Spectrum can be re-purposed either because newer, more spectrally efficient technologies become available (e.g., the transition from analog to digital broadcasting) or it becomes viable to shift operation to other spectrum bands with less commercial value (and, of course, without jeopardizing existing operational excellence). It is also possible that spectrum, previously having been for exclusive federal use (e.g., military applications, fixed satellite uses, etc..), can be shared, such as the case with Citizens Broadband Radio Service (CBRS), which allows non-federal parties access to 150 MHz in the 3.5 GHz band (i.e., band 48). However, it has recently been concluded that (centralized) dynamic spectrum sharing only works in certain use cases and is associated with considerable implementation complexities. Multiple parties with possible vastly different requirements co-existence within a given band is very much work-in-progress and may not be consistent with the commercialized spectrum operation required for high-quality broadband cellular operation.

In parallel with the FCC, we have the National Telecommunications and Information Administration, NTIA for short. NTIA is solely responsible for authorizing Federal spectrum use. It also acts as the President of the United State’s principal adviser on telecommunications policies, coordinating the views of the Executive Branch. NTIA manages about 2,398 MHz (69%) within the so-called “beachfront spectrum” range of 225 MHz to 3.7 GHz (note: I would let that Beachfront go to 7 GHz, to be honest). Of the total of 3,475 MHz, 591 MHz (17%) is exclusively for Federal use, and 1,807 MHz (52%) is shared (or coordinated) between Federal and non-Federal. Thus, leaving 1,077 MHz (31%) for exclusive commercial use under the management of the FCC.

NTIA, in collaboration with the FCC, has been instrumental in the past in freeing up substantial C-band spectrum, 480 MHz in total, of which 100 MHz is conditioned on prioritized sharing (i.e., Auction 105), for commercial and shared use that subsequently has been auctioned off over the last 3 years raising USD 109 billion. In US Dollar (USD) per MHz per population count (pop) we have on average ca. USD 0.68 per MHz-pop from the C-band auctions in the US, compared to USD 0.13 per MHz-pop in Europe C-band auctions, and USD 0.23 per MHz-pop in APAC auctions. It should be remember that the United States exclusive-use spectrum licenses can be regarded as an indefinite-lived intangible asset while European spectrum rights expire between 10 and 20 years. This may explain a big part of the pricing difference between US-based spectrum pricing and that of Europe and Asia.

NTIA and FCC jointly manage all the radio spectrum, licensed (e.g., cellular mobile frequencies, TV signals, …) and unlicensed (e.g., WiFi, MW Owens, …) of the United States, NTIA for Federal use, and FCC for non-Federal use (put simply). FCC is responsible for auctioning spectrum licenses and is also authorized to redistribute licenses.

RESPONSE TO NTIA’s National Spectrum Strategy Request for Comments

Here are some of key points to consider for developing a National Spectrum Strategy (NSS).

  • The NTIA National Spectrum Strategy (NSS) should focus on creating a long-term spectrum pipeline. Developing a coherent national spectrum strategy is critical to innovation, economic competition, national security, and global technology leadership.
  • NTIA should aim at significant amounts of spectrum to study and clear to build a pipeline. Repurposing at least 1,500 Mega Hertz of spectrum perfected for commercial operations is good initial target allowing it to continue to meet consumer, business, and societal demand. It requires more than 1,500 Mega Hertz to be identified for study.
  • NTIA should be aware that the mobile network quality strongly correlates with the mobile operators’ spectrum available for their broadband mobile service in a global setting.
  • NTIA must remember that not all spectrum is equal. As it thinks about a pipeline, it must ensure its plans are consistent with the spectrum needs of various use cases of the wireless sectors. The NSS is a unique opportunity for NTIA to establish a more reliable process and consistent policy for making the federal spectrum available for commercial use. NTIA should reassert its role, and that of the FCC, as the primary federal and commercial regulator of spectrum policy.

A balanced spectrum policy is the right approach. Given the current spectrum dynamics, the NSS should prioritize identifying exclusive-use licensed spectrum instead of, for example, attempting co-existence between commercial and federal use.

Spectrum-band sharing between commercial communications networks and federal communications, or radar systems, may impact the performance of all the involved systems. Such practice compromises the level of innovation in modern commercialized communications networks (e.g., 5G or 6G) to co-exist with the older legacy systems. It also discourages the modernization of legacy federal equipment.

Only high-power licensed spectrum can provide the performance necessary to support nationwide wireless with the scale, reliability, security, resiliency, and capabilities consumers, businesses, and public sector customers expect.

Exclusive use of licensed spectrum provides unique benefits compared to unlicensed and shared spectrum. Unlicensed spectrum, while important, is only suitable for some types of applications, and licensed spectrum under shared access frameworks by CBRS is unsuited for serving as the foundation for nationwide mobile wireless networks.

Allocating new spectrum bands for the exclusive use of licensed spectrum positively impacts the entire wireless ecosystem, including downstream investments by equipment companies and others who support developing and deploying wireless networks. Insufficient licensed spectrum means increasingly deteriorating customer experience and lost economic growth, jobs, and innovation.

Other countries are ahead of the USA in developing plans for licensed spectrum allocations, targeting the full potential of the spectrum range from 300 MHz up to 7 GHz (i.e., the beachfront spectrum range), and those countries will lead the international conversation on licensed spectrum allocation. The NSS offers an opportunity to reassert U.S. leadership in these debates.

NTIA should also consider the substantial benefits and economic value of leading the innovation in modernizing the legacy spectrally in-efficient non-commercial communications and radar systems occupying vast spectrum resources.

Exclusive-use licensed spectrum has inherent characteristics that benefit all users in the wireless ecosystem.

Consumer demand for mobile data is at an all-time high and only continues to surge as demand grows for lightning-fast and responsive wireless products and services enabled by licensed spectrum.

With an appropriately designed and well-sized spectrum pipeline, demand will remain sustainable as supplied spectrum capacity compared to the demand will remain or exceed today’s levels.

Networks built on licensed spectrum are the backbone of next-generation innovative applications like precision agriculture, telehealth, advanced manufacturing, smart cities, and our climate response.

Licensed spectrum is enhancing broadband competition and bridging the digital divide by enabling 5G services like 5G Fixed Wireless Access (FWA) in areas traditionally dominated by cable and in rural areas where fiber is not cost-effective to deploy.

NTIA should identify the midband spectrum (e.g., ~2.5GHz to ~7GHz) and, in particular, frequencies above the C-band for licensed spectrum. That would be the sweet spot for leapfrogging broadband speed and capacity necessary to power 5G and future generations of broadband communications networks.

The National Spectrum Strategy is an opportunity to improve the U.S. Government’s spectrum management process.

The NSS allows NTIA to develop a more consistent and better process for allocating spectrum and providing dispute resolution.

The U.S. should handle mobile networks without a new top-down government-driven industrial policy to manage mobile networks. A central planning model would harm the nation, severely limiting innovation and private sector dynamism.

Instead, we need a better collaboration between government agencies with NTIA and the FCC as the U.S. Government agencies with clear authority over the nation’s spectrum. The NSS also should explore mechanisms to get federal agencies (and their associated industry sectors) to surface their concerns about spectrum allocation decisions early in the process and accept NTIA’s role as a mediator in any dispute.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this article. Of course, throughout the years of being involved in T-Mobile US spectrum strategy, I have enjoyed many discussions and debates with US-based spectrum professionals, bankers, T-Mobile US colleagues, and very smart regulatory policy experts in Deutsche Telekom AG. I have the utmost respect for their work and the challenges they have faced and face. For this particular work, I cannot thank Roslyn Layton, PhD enough for nudging me into writing the comments to NTIA. By that nudge, this little article is a companion to my submission about the US Spectrum as it stands today and what I would like to see with the upcoming National Spectrum Strategy. I very much recommend reading Roslyn’s far more comprehensive and worked-through comments to the NTIA NSS request for advice. A final thank you to John Strand (who keeps away from Linkedin;-) of Strand Consult for challenging my way of thinking and for always stimulating new ways of approaching problems in our telecom sector. I very much appreciate our discussions.

ADDITIONAL MATERIAL.

  1. Kim Kyllesbech Larsen, “NTIA-2023-003. Development of a National Spectrum Strategy (NSS)”, National Spectrum Strategy Request for Comment Responses April 2023. See all submissions here.
  2. Roslyn Layton, “NTIA–2023–0003. Development of a National Spectrum Strategy (NSS)”, National Spectrum Strategy Request for Comment Responses April 2023..
  3. Ronald Harry Coase, “The Federal Communications Commission”, The Journal of Law & Economics, Vol. 2 (October 1959), pp. 1- 40. In my opinion, a must-read for anyone who wants to understand the US spectrum regulation and how it came about.
  4. Kenneth R. Carter, “Policy Lessons from Personal Communications Services: Licensed vs. Unlicensed Spectrum Access,” 2006, Columbus School of Law. An interesting perspective on licensed and unlicensed spectrum access.
  5. Federal Communication Commission (FCC) assigned areas based on the relevant radio licenses. See also FCC Cellular Market Areas (CMAs).
  6. FCC broadband PCS band plan, UL:1850-1910 MHz & DL:1930-1990 MHz, 120 MHz in total or 2×60 MHz.
  7. Understanding Federal Spectrum Use is a good piece from NTIA about the various federal use of spectrum in the United States.
  8. Ookla’s Speedtest Global Index for February 2023. In order to get the historical information use the internet archive, also called “The Wayback Machine.”
  9. I make extensive use of the Spectrum Monitoring site, which I can recommend as one of the most comprehensive sources of frequency allocation data worldwide that I have come across (and is affordable to use).
  10. FCC Releases Rules for Innovative Spectrum Sharing in 3.5 GHz Band.
  11. 47 CFR Part 96—Citizens Broadband Radio Service. Explain the hierarchical spectrum-sharing regime of and priorities given within the CBRS.

Fixed Wireless Access in a Modern 5G Setting – What Does it Bring That We Don’t Already Have?

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Back in 2014, working at Deutsche Telekom AG and responsible for Technology Economics, we looked at alternatives to fiber deployment in Germany (and other markets). It was clear that deploying fiber in Germany would be massively costly and take a very long time… As an incumbent solely relying on xDSL, there was unease in general and in particular with observing that HFC (hybrid-fiber-coaxial) providers were gaining a lot of traction in key markets around Germany. There was an understanding that fiber would be necessary to secure the longer-term survivability of the business. Even as far back as 2011, this was clear to some visionaries within Deutsche Telekom. My interest at the time was whether fixed wireless access (FWA) solutions could be deployed faster (yes, it could and can, at least in Germany) and bridge the time until fiber was sufficiently deployed and with an economically attractive uptake that allowed an operator to retire the FWA solution or re-purpose it for normal mobile access. It economically did not make sense to deploy FWA everywhere … by far not. Though we found that in certain suburban and rural areas, it could make sense to deploy FWA solutions. … So why did it not happen? At the time, the responsible executives for fixed broadband deployment (no, no converged organization at the time) were nervous that “their” fiber Capex would be re-prioritized to FWA and thus taken away from their fiber deployment. Resulting in even further delays in fiber coverage in Germany. Also … they argued the write-off of fiber investments (e.g., 15 – 20+ years) is so much much longer compared to FWA (e.g., 5 – 7 years), and when factoring in the useful lifetime of fiber versus FWA, it made no sense to deploy it (of course ignoring that we could deploy FWA within 6 months while the fiber in that area might not be present in the next 10+ years;-).

I learned three main lessons (a lot more, actually … but that’s for my memoirs if I remember;-)

  • FWA can be made economically favorable but not universally so everywhere.
  • FWA can be a great instrument to bridge the time until fiber deployment has arrived and a given demand (uptake) in an area exists (you just need to make sure your FWA design accounts for the temporary nature of the purpose of your solutions).
  • FWA at high frequencies (e.g., >20 GHz) is not “just” an overlay of an MNOs existing mobile network. The design should be considered a standalone network, with maximum re-use of any existing infrastructure, with line-of-sight (LoS) to customers and LoS redundancy build-in (i.e., multiple redundant paths to a customer).

We are now 10+ years further (and Germany is still Europe’s laggard in terms of fiber deployment and will remain so for many years to come), and the technology landscape that supports both fiber and fixed wireless access is much further as well…

In the following, it is always good to keep in mind that

“Even if your something appears less economically attractive than something else, if that something else is not available or present, your solution may be an interesting opportunity to capture growth to your business. At least within a given window of opportunity.”

and, so it begins …

FIXED WIRELESS ACCESS (FWA).

In this blog, I will define Fixed Wireless Access (FWA) as a service that provides a fixed-like wireless-based internet broadband connection to a household. FWA bypasses the need for a last-mile fixed wired connection from a nearby access point (e.g., street cabinet) to a customer’s household. Thus substituting the need for a fixed copper, coax, or fiber last-mile connection. I will, in general, position FWA in a modern context of 5G, which may enable existing MNOs to bridge the time until they will have fiber coverage, for example, rural and sub-urban areas. Or, as the thinking goes (for some), completely avoid the need for costly and (allegedly) less profitable deployment of fiber in less household-dense areas where more kilometer of fiber needs to be deployed in order to reach the same amount of households compared to an urban or dense urban area. Of course, companies may also be tempted to build FWA-dedicated ISP networks operating in the mmWave range (i.e., >20 GHz) or in the so-called mid-bands range (e.g., ≥ 2.5 GH, C-band, …) to provide higher quality internet services to sub-urban and rural customers where the economics for fiber coverage and connectivity may be comparably challenged in terms of economics and time to fiber availability.

Figure 1 below provides an overview and comparison of the various ways we connect our customers’ homes, with the exception of LEO satellite and stratospheric drone-based connectivity solutions (it’s another very interesting story). So, illustrating terrestrial network-based connectivity to the household with either a fixed-line (buried or aerial) or wireless.

Figure 1 illustrates 3 different ways to connect to a household. The first (Household A) is the “normal” fixed connection, where the last mile from the street cabinet is a physical connection entering the customer’s household either via a buried connection or via a street pole (aerial connection). In the second situation (Household B), the service provider has no fixed assets readily available in a given area but has mobile radio access network infrastructure in the proximity of the household. The provider may choose to offer Fixed Mobile Substitution (FMS) using their existing mobile infrastructure and spectrum capacity to offer households fixed-like service via an in-door modem capable of receiving the radio frequencies upon which the FMS service is offered. Alternatively, and better for the mobile capacity in general (as well as providing a better customer experience), would be to offer the service with an outdoor customer premise antenna (CPA) connecting to an in-door CPE. If the FMS service is provided via a CPA, it may be called or identified as a fixed wireless access (FWA) service. In this connection scenario, cellular spectrum resources are being shared between the household FMS customers and the mobile customer base. The third connectivity scenario (Household C), is where a dedicated high-speed wireless link is established between a service provider’s remote advanced antenna system (and its associated radio access network equipment) and the household’s (typically outdoor) customer premise antenna. Both infrastructure and spectral resources will be dedicated to providing competitive (to broadband fixed alternatives) fixed-like services to customers. This is fixed-wireless access or FWA. In a modern setting service providers would offer fiber-like speeds (e.g., >100 Mbps) with dedicated mmWave 5G (SA) infrastructure. However, it is also possible to provide better-than-average mobile broadband services over a CPA and an operator’s mobile network (as it is often done with 4G or/and cellular 5G NSA).

For the wireless connection between the service provider’s access network and the household, we have several options;

(1) The Fixed Wireless Access (FWA) network provides a dedicated wireless link between the service provider’s network and the customer’s home. In order to maximize the customer experience, typically, an outdoor customer premise antenna (CPA) would have to be installed on the exterior of a household, offering line-of-sight with the provider’s own advanced antenna residing on its access network infrastructure. The provider will likely dedicate a sufficient amount of wireless spectrum bandwidth (in MHz) to provide a competitive (to fixed) broadband service. In a 5G SA (standalone) setting, this could be a cellular spectrum in the mid-band range (≥ 2.5 – 10 GHz) or (or and) mmWave spectrum above 20 GHz. An access network providing fixed-wireless services in the mid-band spectrum typically would overlay an existing mobile network (if the provider is also an MNO) with possibly site additions allowing for higher-availability services to households as well as increase the scale and potential of connecting households due to increased LoS likelihood. In case the services rely on mmWave frequency bands, I would in general, expect a dedicated network infrastructure would have to be built to provide sufficient household scale, reliability, and availability to households in the covered broadband service area. This may (also) rely on existing mobile network infrastructure if the provider is an established MNO, or it may be completely standalone. My rule of thumb is that for every household that is subscribing to the FWA service, I need at least 2, preferably 3, individual line-of-sight solutions to the household CPA. Most conventional cellular network designs (99+% of all there are out in the wild) cannot offer that kind of coverage solution.

The customer premise antenna (CPA) connects to the household’s customer premise equipment (CPE). The CPE provides WiFi coverage within the household either as a single unit or as part of a meshed WiFi household network.

(2) A service that is based on Fixed Mobile Substitution (FMS) utilizes existing cellular resources, such as infrastructure and spectrum bandwidth, to provide a service to a cellular-based (e.g., 4G/5G) customer premise equipment (CPE) residing inside a customer’s household. The CPE connects to the mobile network (via 4G and/or 5G ) and enjoys the quality of the provider’s mobile network. Inside the household, the CPE offers WiFi coverage that is utilized by the household’s occupants. As existing mobile resources are shared with regular mobile customers that may also be in the same household as the FMS solution itself, the service provider needs to carefully balance capacity and quality between the two customer segments, with the household one typically being the greedy one (with respect to network resources and service plans) and impacting network resources substantially more than the regular mobile user (e.g., usually 20+ to 1).

Figure 2 summarizes various connection possibilities there are to connect a household to the internet as well as media content such as linear and streaming TV.

FWA has been around the telco and ISP toolbox for many years in one form or another. The older (or let’s put it nicer, the experienced) reader will remember that a decade ago, many of us believed that WiMax (Worldwide Interoperability for Microwave Access) was the big thing to solve all the ailing (& failings) of 3G, maybe even becoming our industry’s de facto 4G standard. WiMax promised up to 1 Gbps for a fixed (wireless) access base station and up to around 100 Mbps at low mobility (i.e., <50 km per hour). As we know today, it should not be.

FAST FORWARD TO TODAY & TOMORROW WITH 5G AND FIBER SERVICES.

GSMA (GSM Association, the mobile interest group) has been fairly bullish on the advantages and opportunities of 5G-based Fixed Wireless Access (5G-FWA). Alleging a significant momentum behind FWA with (1) 74+ broadband service providers launching FWA services globally, (2) Expecting 40 million 5G FWA subscribers by 2025. Note globally, as of October 2022, there were 5.5 billion unique mobile subscribers. So 5G FWA amounts to <1% of unique subscribers, and last but not least (3) They expect up to 80% cost saving versus fiber to the home (FTTH) @ 100 Mbps downlink. GSMA lists more advantages according with GSMA but the 3 here are maybe the most important.

According to GSMA, in Western Europe, they expect roughly around 275+ million people will subscribe to 5G by 2025. This number represents ca. 140 million unique 5G households. Applying household scaling between western Europe and Global on the global total of 40 million 5G FWA HH, one should expect to capture between 4 to 5 million 5G FWA households or ca. 2.5% FWA HH penetration in Western Europe by 2025 (see below for details of this estimate). This FWA number also corresponds to a ca. 4% of all unique 5G households, or ca. 2% of all unique 5G subscribers, or ca. 1% of all unique mobile subscribers (in 2025). While 40 million (5 million) globally (in Western Europe) sounds like a large number, it is, to all effects rather minuscule and underwhelming compared to the total mobile and fixed broadband market.

The GSMA report, “The 5G FWA opportunity: series highlights” (from July 2022) also provides a 2025 projection for 5G FWA connections as a percentage of households across various countries. In Figure 3 below, find the GSMA projections with, as a comparison, the estimated fiber-to-the-home connections (FTTH) in 2025 and, for reference, the actual FTTH connections in 2021. It seems compelling to assume that 5G FWA would be an alternative to fiber at home or an HFC D3.1 (D = Docsis) connection. Of course, it is only possible to get a service if the technology of choice covers the household. A fiber connection to your household requires that there is a fiber passing in the proximity of your household. Thus the degree of fiber coverage is important in order to assess the fiber subscription uptake possible. Likewise, a 5G FWA connection requires that the household is within a very good and high-quality 5G coverage of the FWA provider (or the underlying network operator). Figure 4 below provides an overview of 2021 actual and 2026 (projected) fiber-based household coverage (i.e., homes passed) percentages in Western Europe.

Figure 3 above shows GSMA 2025 projections of 5G FWA household (HH) connections vs. actual FTTH connections in 2021 and the author’s forecast of FTTH connections by 2025. In countries where the is no 5G-FWA data means, according to GSMA that the expectations are below 1% of HH connected. The total Western Europe 5G FWA connection figure is in excess of 10+ million HH versus 4 – 5 million that was assessed based on the global number of 5G FWA and unique mobile households. In most Western European markets, 5G FWA as defined in the GSMA study, will be a niche service. Note: the FTTH connected percentages are based on total households in the country instead of homes passed figures. Markets that have reached 80% of HHs are capped at that level. In all cases, it would be possible to go beyond. Sources: GSMA for 5G FWA and OECD statistics database.
Figure 4 fiber coverage measured as a percentage of households passed across Western Europe. 2016 and 2021 are actual data based on European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.). The 2026 & 2031 figures are the author’s own forecast based on the last 5 years maximum FTTP/B deployment speed. I have imposed a 95% Household coverage ceiling in my deployment model. The pie charts illustrate the degree the fiber deployment can make use of aerial infrastructure vis-a-vis buried requirements.

If we take a look at 5G coverage, which may be an enabler for FWA services that can compete with fiber quality, it would be fairly okay to assume that most mobile operators in Western Europe would have close to a full 5G population (and households) coverage. However, accessing the 5G quality of that coverage would be problematic. 5G coverage may be based on 700 MHz piggybacking on LTE (i.e., non-standalone, NSA 5G), providing nearly 100% household coverage, it may involve considerable mid-band (i.e., > 2.1 GHz frequency bands) 5G coverage in urban and suburban areas with varying degree of rural coverage, it may also involve the deployment of mmWave (i.e., >20 GHz frequency bands) as an overlay to the normal macro cellular network or as dedicated standalone fixed-wireless access network or a combination of both.

Actually, one might also think that in geographical areas where fiber coverage, or D3.1-based HFC, is relatively limited or completely lacking, 5G FWA opportunities would be more compelling due to the lack of competing broadband alternatives. If the premise is that the 5G FWA service should be fiber-like, it would require good quality 5G coverage with speeds exceeding 100 Mbps at high availability and consistency. However, if the fixed broadband service that FWA would compete with is legacy xDSL, then some of the requirements for fiber-like quality may be relaxed (e.g., 100+ Mbps, very high availability, …).

What are the opportunities, and where? Focusing on fiber deployment in Western Europe, Figure 5 illustrates homes covered by fiber and those with no fiber coverage in urban and rural areas as of 2021 (actual). The figure below also provides a forecast of home coverage and homes missing by 2026.

Figure 5 illustrates the percentage of homes fiber covered (i.e., passed) as well as the homes where fiber coverage remains. The 2021 numbers are actual and based on data in the latest European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.). The 2026 data is the author’s forecast model based on the last 5 years’ fastest fiber rollout speed. 2021 Households numbers (in a million households) are added to the 2021 charts. In general, it is expected that the number of rural households will continue to decline over the period.

As Figure 5 above shows, the urban fiber deployment in Europe is happening at a fast pace in most markets, and the opportunities for alternatives (at scale) may at the same time be seen as diminishing apart from a few laggard markets (e.g., Austria, Belgium, Germany, UK, ..). Rural opportunities for broadband alternatives (to fiber) may be viewed more optimistically with many more households only having access to aging copper lines or relative poor HFC.

A 5G FWA provider may need to think about the window of opportunity to return on the required investment. To address this question, Figure 6 below provides a projection for when at least 80% of households will be connected in urban and rural areas. Showing that in some markets, rural areas may remain more attractive for longer than the corresponding urban areas. Further, if one views the 5G FWA as a bridge to fiber availability, there may be many more opportunities for FWA than what Figures 5 and 6 allude to.

Figure 6 shows projected years until 80% of households have been covered using the maximum deployment pace of the last 5 years. The left side (a) illustrates the urban deployment and (b) the rural fiber deployment. The 80% limit is somewhat arbitrary and, particularly in urban areas, is likely to be exceeded once reached (assuming further deployment is economical). Most commercial (unsubsidized) deployment focus has been in urban areas, while rural areas are often deployed if subsidies are made available by European Union or local government.

Looking at the opportunity for fiber alternatives going forward, Figure 7 below provides the quantum of households that remain to be covered by fiber. This lack of fiber also creates opportunities for broadband alternatives, such as 5G FWA, and maybe non-terrestrial broadband solutions (e.g., Starlink, oneWeb,…). Cellular operators, with a good depth of site coverage, should be able to provide competitive alternatives to existing legacy fixed copper services, as long as LoS is not required, at least. Particularly in some rural areas, depending on the site density and spectrum commitment, around rural villages and towns. Cellular networks may not have much capacity and quality to spare in urban areas for fixed mobile substitution (FMS), at least if designed economically. This said, and depending on the cellular, and fixed broadband competitive environment, FMS-based services (4G and 5G) may be able to bridge the short time until fiber becomes available in an area. This can be used by an incumbent telco that is in the process of migrating its aging copper infrastructure to fiber or as a measure by competing cellular operators to tease copper customers away from that incumbent. Hopefully, those cellular Telcos have also thought about FMS migration off their cellular networks to a permanent fixed broadband solution, such as fiber (or a dedicated mmWave-based FWA service).

Figure 7 estimates the remaining households in (a) urban and (b) rural areas in 2023 and 2026. It may be regarded as a measure of the remaining potential for alternative (to fiber) broadband services. Note: Please note that the scale of Urban and Rural households remaining is different.

As pointed out previously, GSMA projects by 2025 ca. 5 million 5G FWA households in Western Europe. This is less than 3 out of every 100 regular households. Compared with fiber coverage of households estimated to be around 60 out of 100 by 2025. Given that some countries in Western Europe are lagging behind fiber deployment (e.g., Germany, UK, Italy, … see charts above), leaving a large part of their population without modern fixed broadband, one could expect the number might have been bigger than just a few percent. However, 5G FWA at 3.x GHz, and at mmWave frequencies require line-of-sight connections to a customer’s household to provide fiber-like quality and stability. Cellular networks were (obviously) never designed to have LoS to its customers as the cellular frequencies (≤ 3 GHz) were sufficiently low not to be “bothered” (too much) by penetration losses. At and above 3 GHz LoS is increasingly required if a fiber-like service is required.

Another aspect that is often under-appreciated or flat-out ignored (particularly by cellular-minded marketing & sales professionals), is the need for an exterior household customer premise antenna (CPA) that will allow a household to pick up the FWA signal at a higher quality (compared to a gateway antenna indoor due to penetration loss) and with minimum network interference, which may reduce overall quality and capacity in the cellular network (that coincidentally will hurt the normal cellular user as well as other FWA customers). The reason for this neglect is, in my opinion, that it is (allegedly) more difficult to sell such as product to cellular-minded customers and to cellular-minded salespeople as well. It also may increase the cost of technical support due to more complex installation procedures (compared to having a normal mobile phone or indoor gateway) than just turning on a cellular-WiFi modem box inside the home, and it may also result in higher ongoing customer service cost due to more components compared to either a cellular phone or a cellular modem.

THE ECONOMICS.

GSMA Intelligence group compared the total cost of ownership (TCO) of a dedicated 5G FWA mmWave-based connection with that of fiber-to-the-home (FTTH) for an MNO with an existing 5G network in Europe. It appears that the GSMA’s TCO model(s) are rich in detail regarding the underlying traffic models and cost drivers. Moreover, it would also appear that their TCO analysis is (at least at some level) based on an assumed kilometer-based TCO benchmark. It is unclear to me whether Opex has been considered. Though given the analysis is TCO, I assume that it is the case it was considered.

GSMA (for Europe) found that compared to fiber-based household connectivity, 5G FWA is 80% cheaper in rural areas, 60% cheaper in suburban, and 35% cheaper in urban areas compared to an FTTH deployment.

My initial thoughts, without doing any math on the GSMA results, was that I could (easily) imagine that 5G FWA would require less absolute Capex compared to deploying fiber to the home. At least for buried fiber deployment. I would be less confident wrt this result when it comes to aerial fiber deployment, but maybe it is also still a valid result. However, when considering Opex, what 5G FWA incrementally contributes, I would be much less sure that 5G FWA would be outperforming FTTH. At the least in rural and suburban areas where the household customer density per 5G FWA site would be very low (even before considering the opportunity based on LoS likelihood). Thus, the 5G FWA Opex scaled with the number of household subscribers may be a lot less favorable than FTTH, considering the access energy consumption and technical support costs alone. This is even before considering whether a normal rural and a suburban cellular network is at all suitable (designed for) for providing high availability and high-quality+ fixed-like broadband services delivered by 3.x GHz or mmWave frequencies (which in rural and suburban areas may be even more problematic on existing cellular networks).

I would generally not expect that the existing rural/suburban cellular network would be remotely adequate to permanently replace the need for fiber-connected homes. We would most likely need to densify (add new sites) to ensure high quality and high availability connectivity to customers’ premises. This typically would translate into line-of-site (LoS) requirements between the 5G FWA antenna and the customers’ households. Also, to ensure high availability, similar to a fiber connection, we should expect the need for redundant LoS connectivity to the customers’ households (note: experience has shown that having only one LoS connection compromises availability and consistency/reliability substantially). Such redundant connectivity solutions would be even more difficult to find in existing cellular networks. These considerations would, if considered, both add substantial Capex and additional Opex to the 5G FWA TCO reducing the economical (and maybe commercial) attractiveness compared to FTTH.

HOW TO MAKE APPLES AND ORANGES MORE LIKE BANANAS.

As mentioned above, GSMA appears to base (some of) its economic conclusions on a per kilometer (km) unit driver. That is Euro-per-km. While I don’t have anything particular against this driver, apart from being rather 1-dimensional, I believe it provides fewer insights than maybe others’ more direct drivers of income, capital, and operational cost as well as, in the end, a given solution’s commercial success.

I prefer to use the number of households (HH) per square kilometer, thus HH per km2. For fiber deployment and household coverage, I would use fiber per HH passed (HHP). Fiber connecting the household, providing the actual connection (“the last mile”) to customers’ home, I use fiber HH connected (HHC). The intention behind fiber coverage, what is called household passed, is to be able to connect households by extending the fiber to the “last mile” (or the last-1.61-kilometer) and start generating revenues that return on the capital investment done in the first place. Fiber coverage can be thought of as a real option to connect a home. Fiber coverage is obviously a necesity for connecting a home. Similarly, building dedicated fixed-wireless access infrastructure, incrementally on existing cellular infra or from scratch, is to provide a fixed-like high-quality wireless connection to a household.

Figure 8 The above is an illustration of fiber deployment (i.e., coverage and connection) in comparison with fixed wireless access (FWA) coverage and fixed-like wireless services rendered to households (as opposed to individual mobile devices). It also provides a bit of rationale why a km-metric may capture less of the “action” than what happens within a km2 and with the households within. The most important metric in my analysis is the number of connected homes within a km2 as they tend to pay for the party.

Thus household density is a very important driver for the commercial potential, as well as how much of the deployment capital and operational cost can be assigned to a given household in a given geographical area. Urban areas, in general, have more households than suburban and rural areas. The deployment of Capex and Opex in urban areas will be lower per household than in suburban and more rural urbanized areas.

Every household that is fiber covered, implying that the dwelling is within a short reach of the main fiber passing through and ultimately connected, requires an investment with an operational cost associated and revenue for the service is supported by the connection. Fiber total cost of ownership (TCO) will depend on the amount of households covered and the number of households directly connected to a paying customer. For the fiber deployment economics, I am using data from my “Nature of Telecom Capex” (see Figure 16, and please note that the data is for buried fiber) that provides the capital cost of fiber coverage (households passed) and for homes fiber connected, both as a function of household density. For fiber homes passed (HHP) economics, I am renormalizing to fiber homes connected (HHC). Thus if 90% of homes are covered (i.e., passed) in an area and 60% of the homes passed are connected, those connected homes pay for the remaining unconnected homes (30%) under the fiber coverage. This somewhat inflates the cost of connecting a home but is similar to the economic logic of cellular coverage, where the cost is paid by customers having access to the cellular site, even if the cellular site usually covers a lot more people than customers.

In general, fiber deployment becomes increasingly costly as the deployment moves from denser urbanized areas out to suburban and finally rural areas as the household density decreases and more area (and kilometers) need to be covered to capture the same amount of households as in urban areas. Also, it is worth keeping in mind that in countries with the possibility of substantial aerial fiber deployment (e.g., Spain, France, Portugal, Poland, etc..), this leads to a significant unit cost reduction in comparison to buried fiber deployment as we know it from Germany, Netherlands and Denmark. Figure 4 above provides an overview of Western European countries with aerial fiber deployment possibilities and those where buried fiber is required.

For an incremental FWA solution, an existing cellular site will be used. The site location will offer a coverage area where normal broadband cellular services can be provided. Households can of course be connected either via a normal mobile device or a dedicate inhourse gateway connecting to the cellular network (possibly via an exterior CPA) and offering indoor WiFi coverage. For scalable fiber-like wireless quality (e.g., stability and speed) of effective speeds exceeding 100+ Mbps per household connection to be offered from a normal cellular site we typically need line-of-site (LoS) to a customer home as well as a substantial amount of dedicated spectrum bandwidth (100+ MHz) provisioned on an advanced antenna system (AAS e.g., massive MiMo 64×64). The 5G FWA solution, I am assuming, is one that requires the receiving customer to have an outdoor antenna installed on the customer’s home with LoS to the cellular site hosting the FWA solution. The solution is assumed to cover 1 km2 (range of ca. 560 meters) with an effective speed of 300 Mbps per connection. That throughput should hold up to a given connection load limit, after which the speed is expected to decrease as additional household connections are added to the cellular site.

One of, in my opinion, the biggest assumptions (or neglects) of the fiber-like 5G FWA service to households at scale (honestly, a couple of % of HH is not worth discussing;-) is the ability to achieve a line-of-sight between the provider’s cellular site antenna and that of a household with its own customer premise antenna (CPA). For 3.x GHz services, one may assume that everything will still work nicely without LoS and with an inhouse gateway without supporting exterior CPA. I agree … with that premise … if what is required is to beat a xDSL or poor HFC service. There are certainly still many places in Western Europe where that may even make good business sense to attempt to do (that is, competing inferior fixed legacy “broadband” services). The way that cellular networks have been designed (which obviously also have to do with the relative low cellular frequency ranges of the past) is not supporting LoS at scale in urbanized environments. Some great work by professor Dr Akram Al-Hourani, summarised in Figure 9 below, clearly illustrates the difficulty in achieving LoS in urban areas. While I am of the opinion that the basic logic of urban LoS is straightforward, it seems that cellular folks tend to be so used to having (good) cellular coverage pretty much anywhere that it is forgotten when considering higher frequencies that work much better at (or only with) line-of-sight.

The lack of LoS in areas targeted for 5G FWA services needs to be considered in the economic analysis. At least if you are up against fiber-like quality and your intention is to compete at scale (some household opportunity as is the case for fiber). For your FWA cellular-based network, this would often require some degree of densification compared to the as-is cellular network that may be in place. In my work below, I have assumed that my default 5G FWA configuration and targeted service requires 6 sectors covering a 1 km2 of a given urbanized household density. The consequence of that may be that a new (greenfield) site will be required in order to provide 5G FWA at scale (>10+% of HH).

Figure 9 above illustrates the probability in an urban environment for achieving line-of-sight (LoS) between two points, separated by a horizon distance d12 and at height h1 and h2. It is worth keeping in mind that typical urban (and rural) antenna height will be in the range of 30 meter. To give context to the above LoS probability curves, a typical one and two storey will have a height less than 10 meters and 30 meters would represent probably represent 80+% of urbanized areas. The above illustration is inspired by the wonderful work of Dr Akram Al-Hourani Associate Professor and the Telecommunication Program Manager at the School of Engineering, Royal Melbourne Institute of Technology (RMIT) (see his paper “On the Probability of Line-of-Sight in Urban Environments”). There is some relatively simple Monte Carlo simulation work that can be done to verify the above LoS probability trends that I recommend doing.

The economics of this solution is straightforward. I have an upfront investment in enabling the FWA solution with a targeted quality level (e.g., ). In a first approximation and up to a predefined (and pre-agreed as sellable with Marketing), this investment is independent of the number of household customers I get. Of course, at some given load & quality conditions, the FWA capacity may have to be expanded by, for example, adding more capable antennas, more FWA (relevant) spectrum, additional sectors, or building a new site. It should be noted that I have not considered the capacity expansion part in the presented analysis in this article. Thus, as the amount of connected FWA households increases, the quality, in general, and speed, in particular, would decrease (typically by a non-linear process).

Most cellular networks have a substantial part of their infrastructure that does not generate any substantial amount of traffic. In other words, its resources are substantially under-utilized in most cellular networks. Part of building a cellular network is to ensure coverage is guaranteed to almost all of the population (98%+) and geography (>90%), irrespective of the expected demand. Some Telcos’ obsession with public speed & performance tests & benchmarks (e.g., Umlaut, Ookla, etc…) has resulted in many networks having an “insane” (un-demanded and highly un-economical) amount of capacity and quality in coverage areas without any particular customer demand. This typically leads to industry consultants proposing to use all that excess quality for what they may call FWA. I would call it FMS (but what’s in a name). Though, even if there may be a lot of excess cellular capacity and quality in rural and subs-urban areas, it’s hardly fiber-like. And it is also highly unlikely to offer the same scale opportunity in terms of households as a fiber deployment would do (hint: LoS likelihood). The opportunity that is exploitable is to compete with xDSL and poor-quality HFC (if available at all). If an area doesn’t have fiber and no good quality coax, that excess cellular capacity can be used as an alternative to xDSL.

To provide competitive fiber-like FWA services with wireless on top of an existing cellular network, we need to design it “right”. Our aim should be a speed well above 100 Mbps (e.g., 300 Mbps) with stability and availability that requires a different design principle than current legacy cellular networks. To provide a 300 Mbps wireless household connection we could start out with a bandwidth of 100 MHz at 3.5 GHz (i.e., 5G mid-band as an example). Later it is possible to upgrade to or add a mmWave solution with even more bandwidth (e.g., 20 to 300 GHz frequency range with bandwidths of multiples of GHz). In order to get both stability and availability, I will assume that I need a minimum of two but preferably three different LoS solutions for an individual household. If no fiber or other high-quality fixed broadband competitors are around, this requirement may be relaxed (although I think a minimum of two LoS connections are required to provide a real fixed broadband alternative at frequencies above 3 GHz).

SOME COMPARATIVE RESULTS.

In my economic analysis of fiber deployment and 5G-based fixed wireless access, the total cost of ownership (TCO) is presented relative to the number of households connected. This way of presenting the economics has the advantage of relating costs directly to the customer that will pay for the service.

The Capex for fiber deployment can be broken up into two parts. The first part is the fiber coverage, also called fiber household passed (HHP). The second part is household connected (HHC), connecting customer households to the main fiber pass, which is also what we like to call Fiber to the Home (FTTH).

The capital expense of fiber coverage is mainly driven by the civil work (ca. 70%, with the remainder being ca. 20% to passive and ca. 10% for the active part) and relates to the distance fiber is being laid out over (yes, there is a km driver there;-). The cost can be directly related to household density. We have an economic relationship between deployment cost and the actual household density reflecting the difference in unit deployment cost between urban (i.e., high household density, least unit Capex), suburban, and rural (i.e., low household density and highest unit Capex ) urbanized areas. You need fewer kilometers to cover a given amount of households in dense urban areas than is required in a rural village with spread-out dwellings and substantially lower household density. In my economic analysis, I re-scale the fiber coverage cost to the number of households connected (i.e., the customers). Similar to household coverage cost, the household connection cost can likewise be related to the household density, which is already a measure of the connection cost. The details have been described in details in my earlier article, “The Nature of Telecom Capex.”.

The capital expenses related to fixed wireless access will, by its very nature, have a fairly large variation in its various components making up the total investment to provide fixed-like services to customer households. It will depend critically on the design criteria of the service we would like to offer (e.g., max & min speed, availability, … ) as well as the cellular network’s starting point (e.g., greenfield vs brownfield, site density, the likelihood of customer household LoS, etc..). Furthermore, supplier choice, including existing supplier lock-in and corporate purchasing power can influence the unit Capex substantially as well. Civil works and passive infrastructure is reasonably stable across western Europe, with a minor dependency on a given country’s income levels for the civil work-related cost. In my experience, the largest capital expense variation will be on the active telecom equipment depending heavily on procurement scale and supplier leverage. As I have worked in the past for a Telco which is imo&e is one of the strongest (in the industry) in terms of purchasing power and supplier leverage, there is a danger that my unitary Capex assessment may be biased towards the lower end of a reasonable estimate for an industry average for the active equipment required. Another Capex expense factor associated with substantial variation is the spectrum expense I am using in my estimate. My 5G FWA design example requires me to deploy 100 MHz at 3.x GHz (e.g., 3.4 – 3.7 GHz). I have chosen the spectrum cost to be the median of 3.x GHz European spectrum auctions from 2017 to 2023 (a total of 22 in my dataset). The auction median cost is found to be ca. 0.08 € per MHz-pop, and the interquartile range (as a measure for variation) is 0.08 € per MHz-pop. Using an average number of people per Western European household of 2.2, assuming a telco market share of 30%, and a 100 MHz bandwidth, the spectrum cost per connected household would be ca. 60 Euro (per HHC).

In general, the cost of connecting households to fiber scales directly (strongly) with the household density. The cost of connecting a household with fixed wireless access only scales very weakly with the household density (e.g., via CPA, CPE, technical support). Although, if the criteria are that FWA will have to continue to deliver a very high target speed and availability, as the household density increases, there will be substantial step function increases in the required Capex and subsequent resulting Opex. FWA TCO per connected house becomes prohibitively costly as the household density decreases, as is the case for normal cellular services as well.

The total cost of ownership (TCO) includes both the capital as well as the operational expenses relating to the technical implementation of the fixed (FTTH) and fiber-like broadband (5G FWA) service. The various components included in the TCO analysis are summarised in Figure 10.

Figure 10 illustrates the critical parameters used in this analysis and their drivers. As explained, all drivers are re-scaled to be consistent with the household connection. Rather than, for example, the number of households passed for fiber deployment or population coverage for cellular infrastructure deployment. Note 1: for a new 5G FWA site, “Active Equipment” should include a fiber connection & the associated backhaul and possible fronthaul transport equipment. This transport solution is assumed present for an existing site and not included in its economics.

In my analysis, I have compared the cost of implementing different FWA designs with that of connecting a household with fiber. I define a competitive 5G FWA service as a service that can provide a similar quality in terms of speed and stability as that of a GPON-based fiber connection would be able to. The fiber-to-the-home service is designed to bring up to 1 Gbps line speed to a household and could, with the right design, be extended to 10 Gbps with XGPON at a relatively low upgrade capital cost. The FWA service targets an effective speed of 300 Mbps. As household connections are added to the 5G FWA site, at some point, it would become unlikely that the targeted service level can be maintained unless substantial expansions are made to the 5G site, e.g., adding a mmWave solution with a jump in additional frequency spectrum (>100MHz). This would likely lead to additional unit Capex and increases in operational expenses (e.g., energy cost, possible technical support costs, etc..).

Figure 11 compares the TCO, Capex, and Opex of buried fiber to the home (FTTH) to that of fixed wireless access (FWA). For FTTH it is assumed that homes connected amount to 60% of homes passed, which is 90% of the actual household density. The designed FTTH network supports up to 1 Gbps. The FWA is based on LoS to connected homes assuming that I need a total of 6 sectors, one from an existing mobile site and a new 5G site only configured with 5G FWA. The LoS is closed by beamforming from a 64×64 massive MiMo antenna configuration (per sector), with provisioned 100 MHz bandwidth at 3.x GHz, to the customer premise antenna (CPA) installed optimally on the customer household. It is assumed that 30% of covered households will subscribe to the service, and the network cover 98% of all households (with 3-LoS sectors per connected home). The FWA service targets an effective speed of up to 300 Mbps per household. As the number of connected homes increases, there will be a point where the actual serviced speed to the home will be less than 300 Mbps due to the load. The € 30(±8) per month is the Western Europe average cost of a minimum 250 Mbps fixed broadband package. The cities indicate the equivalent household densities. Note: the FWA Opex and, consequently its TCO is different from what has been presented in one of my LinkedIn posts recently. The reason for this is that I spend more time improving my FWA energy consumption model and added some more energy management and steering to my economical model. This is one of the most important cost drivers (also for 5G in general) and I suspect that much more will have to be done in this area to get the overall power consumption substantially down compared to the existing solutions we have today.

Assuming 6 cellular sectors for my chosen 5G FWA solution with 3 of those sectors being greenfield (e.g., abbreviated 3Si + 3Sn), Figure 11 shows that for 5G FWA at scale and targeting competitive services (in terms of quality and stability), is rarely a more economical solution (based on TCO) compared to fiber. Only at high household densities does 5G FWA become economically as attractive as fiber-to-the-home. Although the problem with 5G FWA at large household densities is, that the connection load may be too high to maintain the service design specifications, e.g., speed and availability, without substantial additional upgrades (e.g., mmWave, additional spectrum & sector densification). Even if 5G FWA on a per connected home is (much) more Capex efficient, the economics of Fiber deployment and household fiber connections are more scalable to the connected home than a fixed-like wireless service will be at low and medium household densely urbanized areas.

Relaxing the 5G FWA configuration will not help much as Figure 12 below illustrates. Only in cases where a single existing site (with 3 sectors) can offer a reasonable LoS scale to customer’s households may the TCO be brought down to a comparable range as that of fiber to the home (for a given household density, that is). Using Professor Al-Hourani results one can show that if no receiving household point (e.g., height of building + antenna) is heigher than 15 meter (max. three story buildings) the maximum amount of households with LoS should be no more than 20%. Given that in more rural and suburban environment buildings may be more likely to be a lot lower in exterior height than 15 meter (e.g., 5 – 10 meters) the number of households with LoS (from a single point) could be substantially lower than 20%. In addition, to having a LoS to a household, it, of course, also needs to be your customers premise. Say you have a market share of 30%, one should not expect within a given coverage area to have a potential of more than maybe a maximum of 6% (and likely a lot lower than that). This of course makes any dedicated 5G FWA investment prohibitedly costly due to the lack of scale.

Figure 12 above illustrates a coverage area of 500 connected households and, thus, a relatively dense urban coverage area. FTTH has an uptake of 60% of homes passed, and 5G FWA has a market share of 30% within the covered area. The fiber is relatively straightforward and can be either based on buried or aerial fiber. The depicted figure is based on buried fiber homes connected (FTTH). For FWA we have several options to cover households; (3Si) is based on having 3 sectors with LOS to all household customers. All three sectors are upgraded to support 5G FWA. Based on existing mobile networks and FWA at scale, this would unlikely be the situation. (1Si) is based on one sector covering all connected households (in principle with LoS). One existing sector is upgraded to support 5G FWA. Unless the operator only picks HH with good coverage (e.g., LoS to a given sector) then this scenario appears even more unlikely than the (3Si) scenario at this scale of connected homes, (3Si+3Sn) is based on having an existing site with 3 sectors as well requiring a new 3-sectors site to provide LoS household coverage within the service area. This is also the basis for the FWA cost curves in Figure 10, (3Si+6Sn) based on having an existing site with 3 sectors and requiring two new 3-sectors sites (i.e., additional 6 sectors) to provide LoS household coverage within the service area. Finally, the TCO is compared with (M) a normal mobile 3-sectored 4G & 5G site TCO. The mobile TCO has been normalized to mobile customers assuming a market share of 30%. Note (*): The TCO for the FTTH and all FWA comparisons are based on TCO relative to households connected (HHC).

All in all, using dedicated 5G FWA (or 4G FWA, for that matter) is unlikely to be as economical as a household fiber connection. In rural and suburban areas, where the load may be less of an issue, the existing cellular network’s intercellular distances tend to be too large to be directly usable for fiber-like services. Thus, requiring site densification. In denser urban areas, the connection load may require additional investment to support the demand and maintain quality (e.g., mmWave solutions). However, these places may also be the areas most likely already to be covered by fiber or high quality HFC.

Irrespective of FWA’s maybe poorer economics, in comparison with fiber deployment, there are many countries in Western Europe (and a lot of other places) that lack comprehensive fiber coverage in both urban, suburban and rural areas. Areas that may only be covered by mediocor xDSL services and whatever broadband mobile coverage support. Geograophical areas where fiber may only be deployed years from now if ever at all (unless encourage by EU or other non-commercial subsidies). Such under-served fiber areas may still be commercially interesting for cellular infrastructure telcos, levering existing infra, or dedicated FWA ISPs that may have gotten their hands on lower cost mmWave spectrum.

I should also point out that there is plenty of opportunity for operational expense improvements by deploying for example more intelligent power management systems and/or simply switching off-and-on antenna elements (in the deployed AAS/massive-MiMo antennas) in off-peak traffic hours. The service level that is offered to FWA customers may also be optimized by modern care solutions, e.g., AI chatbots, Apps, IVR, WiFi optimizer solutions, … reducing the need for human-human technical support interactions. However, assuming an FWA customer require a customer premise antenna, requires connectivity to indoor gateway and high quality WiFi coverage in the household, is likely to result in Opex increase in customer care.

IN THE NOW THOUGHTS

I don’t see, FWA, 5G or not, as a credible alternative for fiber to the home. It is doubtful on a household-connection basis that it economically is a better choice. The argument that there is an incredible amount of underutilized resources in our cellular networks, so why not use all that for providing fixed-like, and maybe even fiber-like, services to rural and suburban households, is trying to avoid being held responsible for having possible wasted shareholders money and value but focusing more on being the best irrespective of whether value-generating demand was present or not.

FWA and FMS are technology options that may bridge a time where fiber becomes available in a given geographical footprint. It may act as a precursor for broadband demand that can trigger an accelerate uptake of fiber broadband services once the households have been fiber covered. But its nature as a fiber-like service is likely temporary albeit it may be around for several technology refreshment cycles.

Though, the cellular industry will have to address the relative high operational costs associated with a cellular solution targeting fixed- and fiber-like broadband (and to be honest mobile broadband as well) in comparison with fiber-to-the-home Opex. The projected energy cost of 5G (and 6G for that matter) ecosystem is simply not sustainable nor should it be acceptable to the industry. While suppliers are quick to address the massive improvement in energy consumption per bit-rate per new technology generation, what really is relevant for the network economics is the absolute consumption.

Finally, In time and day, where sustainability and reduction of wasteful demand on critical resources is of incredible importance to our industry, not only for our children’s children but also for achieving favorable financing, shareholders & investors money, consumer trust (and their money month upon month), and possibly the executives self-image, its is difficult to understand why any telco would not prioritize their fiber deployment or fiber service uptake over an incredible resource demanding 5G FWA to either compete or substitute much greener or substantially more sustainable fiber-based services.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Of course, a lot of thanks go out to my former Technology and Network Economics colleagues, who have been a source of inspiration and knowledge. Special thank you to Maurice Ketel (who for many years let my Technology Economics Unit in Deutsche Telekom, I respect him above and beyond), Paul BorkerRemek ProkopikMichael DueserGudrun Bobzin, as well as many many other industry colleagues who have contributed with valuable discussions and important insights. Of course, I can also not get away with (not that I ever would) not thanking Petr Ledl (leading DTAG’s Research & Trials) and Jaroslav Holis (R&T DTAG) for their willingness and a great deal of patience with my many questions into the nature of advanced antenna systems, massive MiMo, what the performance is today and what to expect in terms of performance in the near future. Any mistakes or misrepresentations of these technologies in this article is solely due to me.

FURTHER READING.

FWA EXPECTATIONS – GLOBAL & WESTERN EUROPE

Based on GSMA projections.

The Nature of Telecom Capex.

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CAPEX … IT’S PERSONAL

I built my first Telco Capex model back in 1999. I had just become responsible for what then was called Fixed Network Engineering with a portfolio of all technology engineering design & planning except for the radio access network but including all transport aspects from access up to Core and out to the external world. I got a bit frustrated that every time an assumption changed (e.g., business/marketing/sales), I needed to involve many people in my organization to revise their Capex demand. People that were supposed to get our greenfield network rolled out to our customers. Thus, I built a Capex model that would take the critical business assumptions, size my network (including the radio access network), and consistently assign the right Capex amounts to each category. The model allowed for rapid turnaround on revised business assumptions and a highly auditable track of changes, planning drivers, and unit prices. Since then, I have built best-practice Capex (and technology Opex) models for many Deutsche Telekom AGs and Ooredoo Group entities. Moreover, I have been creating numerous network and business assessment and valuation models (with an eye on M&A), focusing on technology drivers behind Capex and Opex for many different types of telco companies (30+) operating in an extensive range of market environments around the world (20+). Creating and auditing techno-economical models, making those operational and of high quality, it has (for me) been essential to be extensively involved operationally in the telecom sector.

PRELUDE TO CAPEX.

Capital investments, or Capital Expenditures, or just Capex for short, make Telcos go around. Capex is the monetary means used by your Telco to acquire, develop, upgrade, modernize, and maintain tangible, as well as, in some instances, intangible, assets and infrastructure. We can find Capex back under “Property, Plants, and Buildings” (or PPB) in a company’s balance sheet or directly in the profit & loss (or income) statement. Typically for an investment to be characterized as a capital expense, it needs to have a useful lifetime of at least 2 years and be a physical or tangible asset.

What about software? A software development asset is, by definition, intangible or non-physical. However, it can, and often is, assigned Capex status, although such an assignment requires a bit more judgment (and auditorial approvals) than for a real physical asset.

The “Modern History of Telecom” (in Europe) is well represented by Figure 1, showing the fixed-mobile total telecom Capex-to-Revenue ratio from 1996 to 2025.

From 1996 to 2012, most of the European Telco Capex-to-Revenue ratio was driven by investment into mobile technology introductions such as 2G (GSM) in 1996 and 3G (UMTS) in 2000 to 2002 as well as initial 4G (LTE) investments. It is clear that investments into fixed infrastructure, particularly modernizing and enhancing, have been down-prioritized only until recently (e.g., up to 2010+) when incumbents felt obliged to commence investing in fiber infrastructure and urgent modernization of incumbents’ fixed infrastructures in general. For a long time, the investment focus in the telecom industry was mobile networks and sweating the fixed infrastructure assets with attractive margins.

Figure 1 illustrates the “Modern History of Telecom” in Europe. It shows the historical development of Western Europe Telecom Capex to Revenue ratio trend from 1996 to 2025. The maximum was about 28% at the time 2G (GSM) was launched and at minimum after the cash crunch after ultra-expensive 3G licenses and the dot.com crash of 2020. In recent years, since 2008, Capex to Revenue has been steadily increasing as 4G was introduced and fiber deployment started picking up after 20210. It should be emphasized that the Capex to Revenue trend is for both Mobile and Fixed. It does not include frequency spectrum investments.

Across this short modern history of telecom, possibly one of the worst industry (and technology) investments have been the investments we did into 3G. In Europe alone, we invested 100+ billion Euro (i.e., not included in the Figure) into 2100 MHz spectrum licenses that were supposed to provide mobile customers “internet-in-their-pockets”. Something that was really only enabled with the introduction of 4G from 2010 onwards.

Also, from 2010 onwards, telecom companies (in Europe) started to invest increasingly in fiber deployment as well as upgrading their ailing fixed transport and switching networks focusing on enabling competitive fixed broadband services. But fiber investments have picked up in a significant way in the overall telecom Capex, and I suspect it will remain so for the foreseeable future.

Figure 2 When we take the European Telco revenue (mobile & fixed) over the period 1996 to 2025, it is clear that the mobile business model quantum leaped revenue from its inception to around 2008. After this, it has been in steady decline, even if improvement has been observed in the fixed part of the telco business due to the transition from voice-dominated to broadband. Source: https://stats.oecd.org/

As can be observed from Figure 1, since the telecom credit crunch between 2000 and 2003, the Capex share of revenue has steadily increased from just around 12% in 2004, right after the credit crunch, to almost 20% in 2021. Over the period from 2008 to 2021, the industry’s total revenue has steadily declined, as can be seen in Figure 2. Taking the last 10 years (2011-2021) of mobile and fixed revenue data has, on average, reduced by 4+ billion euros a year. The cumulative annual growth rate (CAGR) was at a great +6% from the inception of 2G services in 1996 to 2008, the year of the “great recession.” From 2008 until 2021, the CAGR has been almost -2% in annual revenue loss for Western Europe.

What does that mean for the absolute total Capex spend over the same period? Figure 3 provides the trend of mobile and fixed Capex spending over the period. Since the “happy days” of 2G and 3G Capex spending, Capex rapidly declined after the industry spent 100+ billion Euro on 3G spectrum alone (i.e., 800+ million euros per MHz or 4+ euros per MHz-pop) before the required multi-billion Euro in 3G infrastructure. Though, after 2009, which was the lowest Capex spend after the 3G licenses were acquired, the telecom industry has steadily grown its annual total Capex spend with ca. +1 billion Euro per year (up to 2021) financing new technology introductions (4G and 5G), substantial mobile radio and core modernizations (a big refresh ca. every 6 -7 years), increasing capacity to continuously cope with consumer demand for broadband, fixed transport, and core infrastructure modernization, and last but not least (since the last ~ 8 years) increasing focus on fiber deployment. Over the same period from 2009 to 2021, the total revenue has declined by ca. 5 billion euros per year in Western Europe.

Figure 3 Using the above “Total Capex to Revenue” (Figure 1) and “Total Revenue” (Figure 2) allows us to estimate the absolute “Total Capex” over the same period. Apart from the big Capex swing around the introduction of 2G and 3G and the sharp drop during the “credit crunch” (2000 – 2003), Capex has grown steadily whilst the industry revenue has declined.

It will be very interesting to see how the next 10 years will develop for the telecom industry and its capital investment. There is still a lot to be done on 5G deployment. In fact, many Telcos are just getting started with what they would characterize as “real 5G”, which is 5G standalone at mid-band frequencies (e.g., > 3 GHz for Europe, 2.5 GHz for the USA), modernizing antenna structures from standard passive (low-order) to active antenna systems with higher-order MiMo antennas, possible mmWave deployments, and of course, quantum leap fiber deployment in laggard countries in Europe (e.g., Germany, UK, Greece, Netherlands, … ). Around 2028 to 2030, it would be surprising if the telecoms industry would not commence aggressively selling the consumer the next G. That is 6G.

At this moment, the next 3 to 5 years of Capital spending are being planned out with the aim of having the 2024 budgets approved by November or December. In principle, the long-term plans, that is, until 2027/2028, have agreed on general principles. Though, with the current financial recession brewing. Such plans would likely be scrutinized as well.

I have, over the last year since I published this article, been asked whether I had any data on Ebitda over the period for Western Europe. I have spent considerable time researching this, and the below chart provides my best shot at such a view for the Telecom industry in Western Europe from the early days of mobile until today. This, however, should be taken with much more caution than the above Caex and Revenues, as individual Telco’ s have changed substantially over the period both in their organizational structure and how results have been represented in their annual reports.

Figure 4 illustrates the historical development of the EBITDA margin over the period from 1995 to 2022 and a projection of the possible trends from 2023 onwards.

IT’S THAT TIME OF THE YEAR … CAPEX IS IN THE AIR.

It is the time of the year when many telcos are busy updating their business and financial planning for the following years. It is not uncommon to plan for 3 to 5 years ahead. It involves scenario planning and stress tests of those scenarios. Scenarios would include expectations of how the relevant market will evolve as well as the impact of the political and economic environment (e.g., covid lockdowns, the war in Ukraine, inflationary pressures, supply-chain challenges, … ) and possible changes to their asset ownership (e.g., infrastructure spin-offs).

Typically, between the end of the third or beginning of the fourth quarter, telecommunications businesses would have converged upon a plan for the coming years, and work will focus on in-depth budget planning for the year to come, thus 2024. This is important for the operational part of the business, as work orders and purchase orders for the first quarter of the following year would need to be issued within the current year.

The planning process can be sophisticated, involving many parts of the organization considering many scenarios, and being almost mathematical in its planning nature. It can be relatively simple with the business’s top-down financial targets to adhere to. In most instances, it’s likely a combination of both. Of course, if you are a publicly-traded company or part of one, your past planning will generally limit how much your new planning can change from the old. That is unless you improve upon your old plans or have no choice but to disappoint investors and shareholders (typically, though, one can always work on a good story). In general, businesses tend to be cautiously optimistic about uncertain business drivers (e.g., customer growth, churn, revenue, EBITDA) and conservatively pessimistic on business drivers of a more certain character (e.g., Capex, fixed cost, G&A expenses, people cost, etc..). All that without substantially and negatively changing plans too much between one planning horizon to the next.

Capital expense, Capex, is one of the foundations, or enablers, of the telco business. It finances the building, expansion, operation, and maintenance of the telco network, allowing customers to enjoy mobile services, fixed broadband services, TV services, etc., of ever-increasing quality and diversity. I like to look at Capex as the investments I need to incur in order to sustain my existing revenues, grow my revenues (preferably beating inflationary pressures), and finance any efficiency activities that will reduce my operational expenses in the future.

If we want to make the value of Capex to the corporation a little firmer, we need a little bit of financial calculus. We can write a company’s value (CV) as

With g being the expected growth rate in free cash flow in perpetuity, WACC is the Weighted Average Cost of Capital, and FCFF is the Free Cash Flow to the Firm (i.e., company) that we can write as follows;

FCFF = NOPLAT + Depreciation & Amortization (DA) – ∆ Working Capital – Capex,

with NOPLAT being the Net Operating Profit Less Adjusted Taxes (i.e., EBIT – Cash Taxes). So if I have two different Capex budgets with everything else staying the same despite the difference in Capex (if true life would be so easy, right?);

assuming that everything except the proposed Capex remains the same. With a difference of, for example, 10 Million Euro, a future growth rate g = 0% (maybe conservative), and a WACC of 5% (note: you can find the latest average WACC data for the industry here, which is updated regularly by New York University Leonard N. Stern School of Business. The 5% chosen here serves as an illustration only. You should always choose the weighted average cost of capital that is applicable to your context). The above formula would tell us that the investment plan having 10 Million euros less would be 200 Million euros more valuable (20× the Capex not spent). Anyone with a bit of (hands-on!) experience in budget business planning would know that the above valuation logic should be taken with a mountain of salt. If you have two Capex plans with no positive difference in business or financial value, you should choose the plan with less Capex (and don’t count yourself rich on what you did not do). Of course, some topics may require Capex without obvious benefits to the top or bottom line. Such examples are easy to find, e.g., regulatory requirements or geo-political risks force investments that may appear valueless or even value destructive. Those require meticulous considerations, and timing may often play a role in optimizing your investment strategy around such topics. In some cases, management will create a narrative around a corporate investment decision that fits an optimized valuation, typically hedging on one-sided inflated risks to the business if not done. Whatever decision is made, it is good to remember that Capex, and resulting Opex, is in most cases a certainty. The business benefits in terms of more revenue or more customers are uncertain as is assuming your business will be worth more in a number of years if your antennas are yellow and not green. One may call this the “Faith-based case of more Capex.”

Figure 5 provides an overview of Western Europe of annual Fixed & Mobile Capex, Total and Service Revenues, and Capex to Revenue ratio (in %). Source: New Street Research Western Europe data.

Figure 5 provides an overview of Western European telcos’ revenue, Capex, and Capex to Revenue ratio. Over the last five years, Western European telcos have been spending increasingly higher Capex levels. In 2021 the telecom Capex was 6 billion euros higher than what was spent in 2017, about 13% higher. Fixed and mobile service revenue increased by 14 billion euros, yielding a Capex to Service revenue ratio of 23% in 2021 compared to 20.6% in 2017. In most cases, the total revenue would be reported, and if luck has its way (or you are a subscriber to New Street Research), the total Capex. Thus, capturing both the mobile and the fixed business, including any non-service-related revenues from the company. As defined in this article, non-service-related revenues would comprise revenues from wholesales, sales of equipment (e.g., mobile devices, STB, and CPEs), and other non-service-specific revenues. As a rule of thumb, the relative difference between total and service-related revenues is usually between 1.1 to 1.3 (e.g., the last 5-year average for WEU was 1.17). 

One of the main drivers for the Western European Capex has firstly been aggressive fiber-to-the-premise (FTTP) deployment, typically measured in homes passed across most of the European metropolitan footprint as well as urban areas in general. As fiber covers more and more residential households, increased subscription to fiber occurs as well. This also requires substantial additional Capex for a fixed broadband business. Figure 6 illustrates the annual FTTP (homes passed) deployment volume in Western Europe as well as the total household fiber coverage.

Figure 6 above shows the fiber to the premise (FTTP) home passed deployment per anno from 2017 to 2020 Actual (source: European Commission’s “Broadband Coverage in Europe 2020” authored by Omdia et al.) and 2021 to 2025 projected numbers (i.e., this author’s own assessment). During the period from 2017 to 2020, household fiber coverage grew from 24% to 37% and is expected to grow to at least 68% by 2025.

A large part of the initial deployment has been in relatively dense urban areas as well as relying on aerial fiber deployment outside bigger metropolitan centers. For example, in Portugal, with 82% of households covered with fiber as of 2020, the existing HFC infrastructure (duct, underground passageways, …) was a key enabler for the very fast, economical, and extensive household fiber coverage there. As the fiber rollout continues and even picks up pace towards 2025, I would expect to continue to see a substantial amount of Capex being attributed. In fact, what is often overlooked in the assessment of the Capex volume being committed to fiber deployment, is that the unit-Capex is likely to increase substantially as countries with no aerial deployment option pick up their fiber rollout pace (e.g., Germany, the UK, Netherlands) and countries with an already relatively high fiber coverage go increasingly suburban and rural.

The second main driver is in the domain of mobile network investment. The 5G radio access deployment has been a major driver in 2020 and 2021. It is expected to continue to contribute significantly to mobile operators Capex in the coming 5 years. For most Western European operators, the initial 5G deployment was at 700 MHz, which provides a very good 5G coverage. However, due to limited frequency spectral bandwidth, there are not very impressive speeds unless combined with a solid pre-existing 4G network. The deployment of 5G at 700 MHz has had a fairly modest effect on Mobile Capex (apart from what operators had to pay out in the 5G spectrum auctions to acquire the spectrum in the first place). Some mobile networks would have been prepared to accommodate the 700 MHz spectrum being supported by existing lower-order or classical antenna infrastructure. In 2021 and going forward, we will see an increasing part of the mobile Capex being allocated to 3.X GHz deployment. Far more sophisticated antenna systems, which co-incidentally also are far more costly in unit-Capex terms, will be taken into use, such as higher-order MiMo antennas from 8×8 passive MiMo to 32×32 and 64×64 active antennas systems. These advanced antenna systems will be deployed widely in metropolitan and urban areas. Some operators may even deploy these costly but very-high performing antenna systems in suburban and rural clutter with the intention to provide fixed-wireless access services to areas that today and for the next 5 – 7 years continue to be under-served with respect to fixed broadband fiber services.

Overall, I would also expect mobile Capex to continue to increase above and beyond the pre-2020 level.

As an external investor with little detailed insights into individual telco operations, it can be difficult to assess whether individual businesses or the industry are investing sufficiently into their technical landscape to allow for growth and increased demand for quality. Most publicly available financial reporting does not provide (if at all) sufficient insights into how capital expenses are deployed or prioritized across the many facets of a telco’s technical infrastructure, platforms, and services. As many telcos provide mobile and fixed services based on owned or wholesaled mobile and fixed networks (or combinations there off), it has become even more challenging to ascertain the quality of individual telecom operations capital investments.

Figure 7 illustrates why analysts like to plot Total Revenue against Total Capex (for fixed and mobile). It provides an excellent correlation. Though great care should be taken not to assume causation is at work here, i.e., “if I invest X Euro more, I will have Y Euro more in revenues.” It may tell you that you need to invest a certain level of Capex in sustaining a certain level of Revenue in your market context (i.e., country geo-socio-economic context). Source: New Street Research Western Europe data covering the following countries: AT, BE, DK, FI, FR, DE, GR, IT, NL, NO, PT, ES, SE, CH, and UK.

Why bother with revenues from the telco services? These would typically drive and dominate the capital investments and, as such, should relate strongly to the Capex plans of telcos. It is customary to benchmark capital spending by comparing the Capex to Revenue (see Figure 7), indicating how much a business needs to invest into infrastructure and services to obtain a certain income level. If nothing is stated, the revenue used for the Capex-to-Revenue ratio would be total revenue. For telcos with fixed and mobile businesses, it’s a very high-level KPI that does not allow for too many insights (in my opinion). It requires some de-averaging to become more meaningful.

THE TELCO TECHNOLOGY FACTORY

Figure 8 (below) illustrates the main capital investment areas and cost drivers for telecommunications operations with either a fixed broadband network, a mobile network, or both. Typically, around 90% of the capital expenditures will be invested into the technology factory comprising network infrastructure, products, services, and all associated with information technology. The remaining ca. 10% will be spent on non-technical infrastructures, such as shops, office space, and other non-tech tangible assets.

Figure 8 Telco Capex is spent across physical (or tangible) infrastructure assets, such as communications equipment, brick & mortar that hosts the equipment, and staff. Furthermore, a considerable amount of a telcos Capex will also go to human development work, e.g., for IT, products & services, either carried out directly by own staff or third parties (i.e., capitalized labor). The above illustrates the macro-levels that make out a mobile or fixed telecommunications network, and the most important areas Capex will be allocated to.

If we take the helicopter view on a telco’s network, we have the customer’s devices, either mobile devices (e.g., smartphone, Internet of Things, tablet, … ) or fixed devices, such as the customer premise equipment (CPE) and set-top box. Typically the broadband network connection to the customer’s premise would require a media converter or optical network terminator (ONT). For a mobile network, we have a wireless connection between the customer device and the radio access network (RAN), the cellular network’s most southern point (or edge). Radio access technology (e.g., 3G, 4G, or 5G) is very important determines for the customer experience. For a fixed network connection, we have fiber or coax (cable) or copper connecting the customer’s premise and the fixed network (e.g., street cabinet). Access (in general) follows the distribution of the customers’ locations and concentration, and their generated traffic is aggregated increasingly as we move north and up towards and into the core network. In today’s modern networks, big-fat-data broadband connections interconnect with the internet and big public data centers hosting both 3rd party and operator-provided content, services, and applications that the customer base demands. In many existing networks, data centers inside the operator’s own “walls” likewise will have service and application platforms that provide customers with more of the operator’s services. Such private data centers, including what is called micro data centers (μDCs) or edge DCs, may also host 3rd party content delivery networks that enable higher quality content services to a telco’s customer base due to a higher degree of proximity to where the customers are located compared to internet-based data centers (that could be located anywhere in the world).

Figure 9 illustrates break-out the details of a mobile as well as a fixed (fiber-based) network’s infrastructure elements, including the customers’ various types of devices.

Figure 9 illustrates that on a helicopter level, a fixed and a classical mobile network structure are reasonably similar, with the main difference of one network carrying the mobile traffic and the other the fixed traffic. The traffic in the fixed network tends to be at least ten larger than in the mobile network. They mainly differ in the access node and how it connects to the customer. For fixed broadband, the physical connection is established between, for example, the ONL (Optical Line Terminal) in the optical distribution network and ONT (Optical Line Terminal) at the customer’s home via a fiber line (i.e., wired). The wireless connection for mobile is between the Radio Node’s antenna and the end-user device. Note: AAS: Advanced Antenna System (e.g., MiMo, massive-MiMo), BBU: Base-band unit, CPE: Customer Premise Equipment, IOT: Internet of Things, IX: Internet Exchange, OLT: Optical Line Termination, and ONT: Optical Network Termination (same as ONU: Optical Network Unit).

From Figure 9 above, it should be clear that there are a lot of similarities between the mobile and fixed networks, with the biggest difference being that the mobile access network establishes a wireless connection to the customer’s devices versus the fixed access network physically wired connection to the device situated at the customer’s premises.

This is good news for fixed-mobile telecommunications operators as these will have considerable architectural and, thus, investment synergies due to those similarities. Although, the sad truth is that even today, many fixed-mobile telco companies, particularly incumbent, remain far away from having achieved fixed-mobile network harmonization and conversion.

Moreover, there are many questions to be asked as well as concerns when it comes to our industry’s Capex plans; what is the Capex required to accommodate data growth, are existing budgets allowing for sufficient network densification (to accommodate growth and quality), and what is the Capex trade-off between frequency spectrum acquisition, antenna technology, and site densification, how much Capex is justified to pursue the best network in a given market, what is the suitable trade-off between investing in fiber to the home and aggressive 5G deployment, should (incumbent) telco’s pursue fixed wireless access (FWA) and how would that impact their capital plans, what is the right antenna strategy, etc…

On a high level, I will provide guidance on many of the above questions, in this article and in forthcoming ones.

THE CAPEX STRUCTURE OF A TELECOM COMPANY.

When taking a macro look at Capex and not yet having a good idea about the breakdown between mobile and fixed investment levels, we are helped that on a macro level, the Capex categories are similar for a fixed and a mobile network. Apart from the last mile (access) in a fixed network is a fixed line (e.g., fiber, coax, or copper) and a wireless connection in a mobile network; the rest is comparable in nature and function. This is not surprising as a business with a fixed-mobile infrastructure would (should!) leverage the commonalities in transport and part of the access architecture.

In the fixed business, devices required to enable services on the fixed-line network at the fixed customers’ home (e.g., CPE, STB, …) are a capital expense driven by new customers and device replacement. This is not the case for mobile devices (i.e., an operational expense).

Figure 10 above illustrates the major Capex elements and their distribution defined by the median, lower and upper quantiles (the box), and lower and upper extremes (the whiskers) of what one should expect of various elements’ contribution to telco Capex. Note: CPE: Customer Premise Equipment, STB: Set-Top Box.

CUSTOMER PREMISE EQUIPMENT (CPE) & SET-TOP BOXES (STB) investments ARE between 10% to 20% of the TelEcoM Capex.

The capital investment level into Customer premise equipment (CPE) depends on the expected growth in the fixed customer base and the replacement of old or defective CPEs already in the fixed customer base. We would generally expect this to make out between 10% to 20% of the total Capex of a fixed-mobile telco (and 0% in a mobile-only business). When migrating from one access technology (e.g., copper/xDSL phase-out, coaxial cable) to another (e.g., fiber or hybrid coaxial cable), more Capex may be required. Similar considerations for set-top boxes (STB) replacement due to, for example, a new TV platform, non-compliance with new requirements, etc. Many Western European incumbents are phasing out their extensive and aging copper networks and replacing those with fiber-based networks. At the same time, incumbents may have substantial capital requirements phasing out their legacy copper-based access networks, the capital burden on other competitor telcos in markets where this is happening if such would have a significant copper-based wholesale relationship with the incumbent.

In summary, over the next five years, we should expect an increase in CPE-based Caped due to the legacy copper phase-out of incumbent fixed telcos. This will also increase the capital pressure in transport and access categories.

CPE & STB Capex KPIs: Capex share of Total and Capex per Gross Added Customer.

Capex modeling comment: Use your customer forecast model as the driver for new CPEs. Your research should give you an idea of the price range of CPEs used by your target fixed broadband business. Always include CPE replacement in the existing base and the gross adds for the new CPEs. Many fixed broadband retail businesses have been conservative in the capabilities of CPEs they have offered to their customer base (e.g., low-end cheaper CPEs, poor WiFi quality, ≤1Gbps), and it should be considered that these may not be sufficient for customer demand in the following years. An incumbent with a large install base of xDSL customers may also have a substantial migration (to fiber) cost as CPEs are required to be replaced with fiber cable CPEs. Due to the current supply chain and delivery issues, I would assume that operators would be willing to pay a premium for getting critical stock as well as having priority delivery as stock becomes available (e.g., by more expensive shipping means).

Core network & service platformS, including data centers, investments ARE between 8% to 12% of the telecom Capex.

Core network and service platforms should not take up more than 10% of the total Capex. We would regard anything less than 5% or more than 15% as an anomaly in Capital prioritization. This said, over the next couple of years, many telcos with mobile operations will launch 5G standalone core networks, which is a substantial change to the existing core network architecture. This also raises the opportunity for lifting and shifting from monolithic systems or older cloud frameworks to cloud-native and possibly migrating certain functions onto public cloud domains from one or more hyperscalers (e.g., AWS, Azure, Google). As workloads are moved from telco-owned data centers and own monolithic core systems, telco technology cost structure may change from what prior was a substantial capital expense to an operational expense. This is particularly true for software-related developments and licensing.

Another core network & service platform Capex pressure point may come from political or investor pressure to replace Chinese network elements, often far removed from obsolescence and performance issues, with non-Chinese alternatives. This may raise the Core network Capex level for the next 3 to 5 years, possibly beyond 12%. Alas, this would be temporary.

In summary, the following topics would likely be on the Capex priority list;

1. Life-cycle management investments (I like to call Business-as-Usual demand) into software and hardware maintenance, end-of-life replacements, growth (software licenses, HW expansions), and miscellaneous topics. This area tends to dominate the Capex demand unless larger transformational projects exist. It is also the first area to be de-prioritized if required. Working with Priority 1, 2, and 3 categorizations is a good Capital planning methodology. Where Priority 1 is required within the following budget year 1, Prio. 2 is important but can wait until year two without building up too much technical debt and Prio. 3 is nice to have and not expected to be required for the next two subsequent budget years.

2. 5G (Standalone, SA) Core Network deployment (timeline: 18 – 24 months).

3. Network cloudification, initially lift-and-shift with subsequent cloud-native transformation. The trigger point will be enabling the deployment of the 5G standalone (SA) core. Operators will also take the opportunity to clean up their data centers and network core location (timeline: 24 – 36 months).

4. Although edge computing data centers (DC) typically are supposed to support the radio access network (e.g., for Open-RAN), the capital assignment would be with the core network as the expertise for this resides here. The intensity of this Capex (if built by the operator, otherwise, it would be Opex) will depend on the country’s size and fronthaul/backhaul design. The investment trigger point would generally commence on Open-RAN deployment (e.g., 1&1 & Telefonica Germany). The edge DC (or μDC) would most like be standard container-sized (or half that size) and could easily be provided by independent towerco or specific edge-DC 3rd party providers lessening the Capex required for the telco. For smaller geographies (e.g., Netherlands, Denmark, Austria, …), I would not expect this item to be a substantial topic for the Capex plans. Mainly if Open-RAN is not being pursued over the next 5 – 10 years by mainstream incumbent telcos.

5. Chinese supplier replacement. The urgency would depend on regulatory pressure, whether compensation is provided (unlikely) or not, and the obsolescence timeline of the infrastructure in question. Given the high quality at very affordable economics, I expect this not to have the biggest priority and will be executed within timelines dictated more by economics and obsolescence timelines. In any case, I expect that before 2025 most European telcos will have phased out Chinese suppliers from their Core Networks, incl. any Service platforms in use today (timeline: max. 36 months).

6. Cybersecurity investments strengthen infrastructure, processes, and vital data residing in data centers, service platforms, and core network elements. I expect a substantial increase in Capex (and Opex) arising from the telco’s focus on increasing the cyber protection of their critical telecom infrastructure (timeline: max 18 months with urgency).

Core Capex KPIs: Capex share of Total (knowing the share, it is straightforward to get the Capex per Revenue related to the Core), Capex per Incremental demanded data traffic (in Gigabits and Gigabyte per second), Capex per Total traffic, Capex per customer.

Capex modeling comment: In case I have little specific information about an operator’s core network and service platforms, I would tend to model it as a Euro per Customer, Euro per-incremental customer, and Euro per incremental traffic. Checking that I am not violating my Capex range that this category would typically fall within (e.g., 8% to 12%). I would also have to consider obsolescence investments, taking, for example, a percentage of previous cumulated core investments. As mobile operators are in the process, or soon will be, of implementing a 5G standalone core, having an idea of the number of 5G customers and their traffic would be useful to factor that in separately in this Capex category.

Estimating the possible Capex spend on Edge-RAN locations, I would consider that I need ca. 1 μDC per 450 to 700 km2 of O-RAN coverage (i.e., corresponding to a fronthaul distance between the remote radio and the baseband unit of 12 to 15 km). There may be synergies between fixed broadband access locations and the need for μ-datacenters for an O-RAN deployment for an integrated fixed-mobile telco. I suspect that 3rd party towercos, or alike, may eventually also offer this kind of site solutions, possibly sharing the cost with other mobile O-RAN operators.

Transport – core, metro & aggregation investments are between 5% to 15% of Telecom Capex.

The transport network consists of an optical transport network (OTN) connecting all infrastructure nodes via optical fiber. The optical transport network extends down to the access layer from the Core through the Metro and Aggregation layers. On top, the IP network ensures logical connection and control flow of all data transported up and downstream between the infrastructure nodes. As data traffic is carried from the edge of the network upstream, it is aggregated at one or several places in the network (and, of course, disaggregated in the downstream direction). Thus, the higher the transport network, the more bandwidth is supported on the optical and the IP layers. Most of the Capex investment needs would ensure that sufficient optical and IP capacity is available, supporting the growth projections and new service requirements from the business and that no bottlenecks can occur that may have disastrous consequences on customer experience. This mainly comes down to adding cards and ports to the already installed equipment, upgrading & replacing equipment as it reaches capacity or quality limitations, or eventually becoming obsolete. There may be software license fees associated with growth or the introduction of new services that also need to be considered.

Figure 11 above illustrates (high-level) the transport network topology with the optical transport network and IP networking on top. Apart from optical and IP network equipment, this area often includes investments into IP application functions and related hardware (e.g., BNG, DHCP, DNS, AAA RADIUS Servers, …), which have not been shown in the above. In most cases, the underlying optical fiber network would be present and sufficiently scalable, not requiring substantial Capex apart from some repair and minor extensions. Note DWDM: Dense Wavelength-Division multiplexing is an optical fiber multiplexing technology that increases the bandwidth utilization of a FON, BNG: Border Network Gateway connecting subscribers to a network or an internet service providers (ISP) network, important in wholesale arrangements where a 3rd party provides aggregation and access. DHCP: Dynamic Host Configuration Protocol providing IP address allocation and client configurations. AAA: Authentication, Authorization, and Accounting of the subscriber/user, RADIUS: Remote Authentication Dial-In User Service (Server) providing the AAA functionalities.

Although many telcos operate fixed-mobile networks and might even offer fixed-mobile converged services, they may still operate largely separate fixed and mobile networks. It is not uncommon to find very different transport design principles as well as supplier landscapes between fixed and mobile. The maturity, when each was initially built, and technology roadmaps have historically been very different. The fixed traffic dynamics and data volumes are several times higher than mobile traffic. The geographical presence between fixed and mobile tends to be very different (unless the telco of interest is the incumbent with a considerable copper or HFC network). However, the biggest reason for this state of affairs has been people and technology organizations within the telcos resisting change and much more aggressive transport consolidation, which would have been possible.

The mobile traffic could (should!) be accommodated at least from the metro/aggregation layers and upstream through the core transport. There may even be some potential for consolidation on front and backhauls that are worth considering. This would lead to supplier consolidation and organizational synergies as the technology organizations converged into a fixed-mobile engineering organization rather than two separate ones.

I would expect the share of Capex to be on the higher end of the likely range and towards the 10+% at least for the next couple of years, mainly if fixed and mobile networks are being harmonized on the transport level, which may also create an opportunity reduce and harmonize the supplier landscape.

In summary, the following topics would likely be on the Capex priority list;

  1. Life-cycle management (business-as-usual) investments, accommodating growth including new service and quality requirements (annual business-as-usual). There are no indications that the traffic or mobile traffic growth rate over the next five years will be very different from the past. If anything, the 5-year CAGR is slightly decreasing.
  2. Consolidating fixed and mobile transport networks (timelines: 36 to 60 months, depending on network size and geography). Some companies are already in the process of getting this done.
  3. Chinese supplier replacement. To my knowledge, there are fewer regulatory discussions and political pressure for telcos to phase out transport infrastructure. Nevertheless, with the current geopolitical climate (and the upcoming US election in 2024), telcos need to consider this topic very carefully; despite economic (less competition, higher cost), quality, and possible innovation, consequences may result in a departure from such suppliers. It would be a natural consideration in case of modernization needs. An accelerated phase-out may be justified to remove future risks arising from geopolitical pressures.

While I have chosen not to include the Access transport under this category, it is not uncommon to see its budget demand assigned to this category, as the transport side of access (fronthaul and backhaul transport) technically is very synergetic with the transport considerations in aggregation, metro, and core.

Transport Capex KPIs: Capex share of Total, the amount of Capex allocated to Mobile-only and Fixed-only (and, of course, to a harmonized/converged evolved transport network), The Utilization level (if data is available or modeled to this level). The amount of Capex-spend on fiber deployment, active and passive optical transport, and IP.

Capex modeling comment: I would see whether any information is available on a number of core data centers, aggregation, and metro locations. If this information is available, it is possible to get an impression of both core, aggregation, and metro transport networks. If this information is not available, I would assume a sensible transport topology given the particularities of the country where the operator resides, considering whether the operator is an incumbent fixed operator with mobile, a mobile-only operation, or a mobile operator that later has added fixed broadband to its product portfolio. If we are not talking about a greenfield operation, most, if not all, will already be in place, and mainly obsolescence, incremental traffic, and possible transport network extensions would incur Capex. It is important to understand whether fixed-mobile operations have harmonized and integrated their transport infrastructure or large-run those independently of each other. There is substantial Capex synergy in operating an integrated transport network, although it will take time and Capex to get to that integration point.

Access investments are typically between 35% to 50% of the Telecom Capex.

Figure 12 (above) is similar to Figure 8 (above), emphasizing the access part of Fixed and Mobile networks. I have extended the mobile access topology to capture newer development of Open-RAN and fronthaul requirements with pooling (“centralizing”) the baseband (BBU) resources in an edge cloud (e.g., container-sized computing center). Fronthaul & Open-RAN poses requirements to the access transport network. It can be relatively costly to transform a legacy RAN backhaul-only based topology to an Open-RAN fronthaul-based topology. Open-RAN and fronthaul topologies for Greenfield deployments are more flexible and at least require less Capex and Opex. 

Mobile Access Capex.

I will define mobile access (or radio access network, RAN) as everything from the antenna on the site location that supports the customers’ usage (or traffic demand) via the active radio equipment (on-site or residing in an edge-cloud datacenter), through the fronthaul and backhaul transport, up to the point before aggregation (i.e., pre-aggregation). It includes passive and active infrastructure on-site, steal & mortar or storage container, front- and backhaul transport, data center software & equipment (that may be required in an edge data center), and any other hardware or software required to have a functional mobile service on whatever G being sold by the mobile operator.

Figure 13 above illustrates a radio access network architecture that is typically deployed by an incumbent telco supporting up to 4G and 5G. A greenfield operation on 5G (and maybe 4G) could (maybe should?) choose to disaggregate the radio access node using an open interface, allowing for a supplier mix between the remote radio head (RRH and digital frontend) at the site location and the centralized (or distributed) baseband unit (BBU). Fronthaul connects the antenna and RRH with a remote BBU that is situated at an edge-cloud data center (e.g., storage container datacenter unit = micro-data center, μDC). Due to latency constraints, the distance between the remote site and the BBU should not be much more than 10 km. It is customary to name the 5G new radio node a gNB (g-Node-B) like the 4G radio node is named eNB (evolved-Node-B).

When considering the mobile access network, it is good to keep in mind that, at the moment, there are at least two main flavors (that can be mixed, of course) to consider. (1) A classical architecture with the site’s radio access hardware and software from a single supplier, with a remote radio head (RRH) as well as digital frontend processing at or near the antenna. The radio nodes do not allow for mixing suppliers between the remote RF and the baseband. Radio nodes are connected to backhaul transmission that may be enabled by fiber or microwave radios. This option is simple and very well-proven. However, it comes with supplier lock-in and possibly less efficient use of baseband resources as these are likewise fixed to the radio node that the baseband unit is installed. (2) A new Open- or disaggregated radio access network (O-RAN), with the Antenna and RHH at the site location, then connected via fronthaul (≤10 km distance) to a μDC that contains the baseband unit. The μDC would then be connected to the backhaul that connects northbound to aggregation and core. The open interface between the RRH (and digital frontend) and the BBU allows different suppliers and hosts the RAN-specific software on common off-the-shelf (COTS) computing equipment. It allows (in theory) for better scaling and efficiency with the baseband resources. However, the framework has not been standardized by the usual bodies of standardization (e.g., 3GPP) and is not universally accepted as a common standard that all telco suppliers would adhere to. It also has not reached maturity yet (sort of obvious) and is currently (as of July 2022) seen to be associated with substantial cyber-security risks (re: maturity). It may be an interesting deployment model for greenfield operations (e.g., Rakuten Mobile Japan, Jio India, 1&1 Germany, Dish Mobile USA).

For established incumbent mobile operators, I do not see Option (2) as very attractive, at least for the next 5 – 7 years when many legacy technologies (i.e., non-5G) remain to be supported. The main concern should be the maturity, lack of industry-wise standardization, as well as cost of transforming existing access transport networks into compliance with a fronthaul framework. Most likely, some incumbents, the “brave” ones, will deploy O-RAN for 1 or a few 5G bands and keep their legacy networks as is. Most incumbent mobile operators will choose (actually have chosen already) conventional suppliers and the classical topology option to provide their 5G radio access network as it has the highest synergy with the access infrastructure already deployed. Thus, if my assertion is correct, O-RAN will only start becoming mainstream in 5 to 7 years, when existing deployments become obsolete.

Planning the mobile-radio access networks Capex requirements is not (that) difficult. Most of it can be mathematically derived and be easily assessed against growth expectations, expected (or targeted) network utilization (or efficiency), and quality. The growth expectations (should) come from consumer and retail businesses’ forecast of mobile customers over the next 3 to 5 years, their expected usage (if they care, otherwise technology should), or data-plan distribution (maybe including technology distributions, if they care. Otherwise, technology should), as well as the desired level of quality (usually the best).

Figure 14 above illustrates a typical cellular planning structural hierarchy from the sector perspective. One site typically has 3 sectors. One sector can have multiple cells depending on the frequency bands installed in the (multi-band) antennas. Massive MiMo antenna systems provide target cellular beams toward the user’s device that extend the range of coverage (via the beam). Very fast scheduling will enable beams to be switched/cycled to other users in the covered sector (a bit oversimplified). Typically, the sector is planned according to the cell utilization, thus on a frequency-by-frequency basis.

Figure 15 illustrates that most investment drivers can be approached as statistical distributions. Those distributions will tell us how much investment is required to ensure that a critical parameter X remains below a pre-defined critical limit Xc within a given probability (i.e., the proportion of the distribution exceeding Xc). The planning approach will typically establish a reference distribution based on actual data. Then based on marketing forecasts, the planners will evolve the reference based on the expected future usage that drives the planning parameter. Example: Let X be the customer’s average speed in a radio cell (e.g., in a given sector of an antenna site) in the busy hour. The business (including technology) has decided it will target 98% of its cells and should provide better than 10 Mbps for more than 50% of the active time a customer uses a given cell. Typically, we will have several quality-based KPIs, and the more breached they are, the more likely it will be that a Capex action is initiated to improve the customer experience.

Network planners will have access to much information down to the cell level (i.e., the active frequency band in a given sector). This helps them develop solid planning and statistical models that provide confidence in the extrapolation of the critical planning parameters as demand changes (typically increases) that subsequently drive the need for expansions, parameter adjustments, and other optimization requirements. As shown in Figure 15 above, it is customary to allow for some cells to breach a defined critical limit Xc, usually though it is kept low to ensure a given customer experience level. Examples of planning parameters could be cell (and sector) utilization in the busy hour, active concurrent users in cell (or sector), duration users spend at a or lower deemed poor speed level in a given cell, physical resource block (the famous PRB, try to ask what it stands for & what it means😉) utilization, etc.

The following topics would likely be on the Capex priority list;

  1. New radio access deployment Capex. This may be for building new sites for coverage, typically in newly built residential areas, and due to capacity requirements where existing sites can no longer support the demand in a given area. Furthermore, this Capex also covers a new technology deployment such as 5G or deploying a new frequency band requiring a new antenna solution like 3.X GHz would do. As independent tower infrastructure companies (towerco) increasingly are used to providing the required passive site infrastructure solution (e.g., location, concrete, or steel masts/towers/poles), this part will not be a Capex item but be charged as Opex back to the mobile operator. From a European mobile radio access network Capex perspective, the average cost of a total site solution, with active as well as passive infrastructure, should have been reduced by ca. 100 thousand plus Euro, which may translate into a monthly Opex charge of 800 to 1300 Euro per site solution. It should be noted that while many operators have spun off their passive site solutions to third parties and thus effectively reduced their site-related Capex, the cost of antennas has increased dramatically as operators have moved away from classical simple SiSo (Single-in Singe-out) passive antennas to much more advanced antenna systems supporting multiple frequency bands, higher-order antennas (e.g., MiMo) and recently also started deploying active antennas (i.e., integrated amplifiers). This is largely also driven by mobile operators commissioning more and more frequency bands on their radio-access sites. The planning horizon needs at least to be 2 years and preferably 3 to 5 years.
  2. Capex investments that accommodate anticipated radio access growth and increased quality requirements. It is normal to be between 18 – 24 months ahead of the present capacity demand overall, accepting no more than 2% to 5% of cells (in BH) to breach a critical specification limit. Several such critical limits would be used for longer-term planning and operational day-to-day monitoring.
  3. Life-cycle management (business-as-usual) investments such as software annual fees, including licenses that are typically structured around the technologies deployed (e.g., 2G, 3G, 4G, and 5G) and active infrastructure modernization replacing radio access equipment (e.g., baseband units, radio units, antennas, …) that have become obsolete. Site reworks or construction optimization would typically be executed (on request from the operator) by the Towerco entity, where the mobile operator leases the passive site infrastructure. Thus, in such instances may not be a Capex item but charged back as an Operational expense to the telco.
  4. Even if there have been fewer regulatory discussions and political pressure for telcos to phase out radio access, Chinese supplier replacement should be considered. Nevertheless, with the current geopolitical climate (and the upcoming US election), telcos need to consider this topic very carefully; despite economic (less competition, higher cost), quality, and possible innovation, consequences may result in a departure from such suppliers. It would be a natural consideration in case of modernization needs. An accelerated phase-out may be justified to remove future risks arising from geopolitical pressures, although it would result in above-and-beyond capital commitment over a shorter period than otherwise would be the case. Telco valuation may suffer more in the short to medium term than otherwise would have been the case with a more natural phaseout due to obsolescence.

Mobile Access Capex KPIs: Capex share of Total, Access Utilization (reported/planned data traffic demand to the data traffic that could be supplied if all or part of the spectrum was activated), Capex per Site location, Capex per Incremental data traffic demand (in Gigabyte and Gigabit per second which is the real investment driver), Capex per Total Traffic (in Gigabyte and Gigabit per second), Capex per Mobile Customer and Capex to Mobile Revenue (preferably service revenue but the total is fine if the other is not available). As a rule of thumb, 50% of a mobile network typically covers rural areas, which also may carry less than 20% of the total data traffic.

Whether actual and planned Capex is available or an analyst is modeling it, the above KPIs should be followed over an extended period. A single year does not tell much of a story.

Capex modeling comment: When modeling the Capex required for the radio access network, you need to have an idea about how many sites your target telco has. There are many ways to get to that number. In most European countries, it is a matter of public record. Most telcos, nowadays, rarely build their own passive site infrastructure but get that from independent third-party tower companies (e.g., CellNex w. ca. 75k locations, Vantage Towers w. ca. 82k locations, … ) or site-share on another operators site locations if available. So, modeling the RAN Capex is a matter of having a benchmark of the active equipment, knowing what active equipment is most likely to be deployed and how much. I see this as being an iterative modeling process. Given the number of sites and historical Capex, it is possible to come to a reasonable estimate of both volumes of sites being changed and the range of unit-Capex (given good guestimates of active equipment pricing range). Of course, in case you are doing a Capex review, the data should be available to you, and the exercise is straightforward. The mobile Capex KPIs above will allow for consistency checks of a modeling exercise or guide a Capex review process.

I recommend using the classical topology described above when building a radio access model. That is unless you have information that the telco under analysis is transforming to a disaggregated topology with both fronthaul and backhaul. Remember you are not only required to capture the Capex for what is associated with the site location but also what is spent on the access transport. Otherwise, there is a chance that you over-estimate the unit-Capex for the site-related investments.

It is also worth keeping in mind that typically, the first place a telecom company would cut Capex (or down-prioritize) is pressured during the planning process would be in the radio access network category. The reason is that the site-related unitary capex tends to be incredibly well-defined. If you reduce your rollout to 100 site-related units, you should have a very well-defined quantum of Capex that can be allocated to another category. Also, the operational impact of cutting in this category tends to be very well-defined. Depending on how well planned the overall Capex has been done, there typically would be a slack of 5% to 10% overall that could be re-assigned or ultimately reduced if financial results warrant such a move.

Fixed Access Capex.

As mobile access, fixed access is about getting your service out to your customers. Or, if you are a wholesale provider, you can provide the means of your wholesale customer reaching their customer by providing your own fixed access transport infrastructure. Fixed access is about connecting the home, the office, the public institution (e.g., school), or whatever type of dwelling in general.

Figure 16 illustrates a fixed access network and its position in the overall telco architecture. The following make up the ODN (Optical Distribution Network); OLT: Optical Line Termination, ODF: Optical Distribution Frame, POS: Passive Optical Splitter, ONT: Optical Network Termination. At the customer premise, besides the ONT, we have the CPE: Customer Premise Equipment and the STB: Set-Top Box. Suppose you are an operator that bought wholesale fixed access from another telco’ (incl. Open Access Providers, OAPs). In that case, you may require a BNG to establish the connection with your customer’s CPE and STB through the wholesale access network.

As fiber optical access networks are being deployed across Europe, this tends to be a substantial Capex item on the budgets of telcos. Here we have two main Capex drivers. First is the Capex for deploying fibers across urban areas, which provides coverage for households (or dwellings) and is measured as Capex-per-homes passed. Second is the Capex required for establishing the connection to households (or dwellings). The method of fiber deployment is either buried, possibly using existing ducts or underground passageways, or via aerial deployment using established poles (e.g., power poles or street furniture poles) or new poles deployed with the fiber deployment. Aerial deployment tends to incur lower Capex than buried fiber solutions due to requiring less civil work. The OLT, ODF, POS, and optical fiber planning, design, and build to provide home coverage depends on the home-passed deployment ambition. The fiber to connect a home (i.e., civil work and materials), ONT, CPE, and STBs are driven by homes connected (or FTTH connected). Typically, CPE and STBs are not included in the Access Capex but should be accounted for as a separate business-driven Capex item.

The network solutions (BNG, OLT, Routers, Switches, …) outside the customer’s dwelling come in the form of a cabinet and appropriate cards to populate the cabinet. The cards provide the capacity and serviced speed (e.g., 100 Mbps, 300 Mbps, 1 Gbps, 10 Gbps, …) sold to the fixed broadband customer. Moreover, for some of the deployed solutions, there is likely a mandatory software (incl. features) fee and possibly both optional and custom-specific features (although rare to see that in mainstream deployments). It should be clear (but you would be surprised) that ONT and CPE should support the provisioned speed of the fixed access network. The customer cannot get more quality than the minimum level of either the ONT, CPE, or what the ODN has been built to deliver. In other words, if the networking cards have been deployed only to support up to 1 Gbps and your ONT, and CPE may support 3 Gbps or more, your customer will not be able to have a service beyond 1 Gbps. Of course, the other way around as well. I cannot stress enough the importance of longer-term planning in this respect. Your network should be as flexible as possible in providing customer services. It may seem that Capex savings can be made by only deploying capacity sold today or may be required by business over the next 12 months. While taking a 3 to 5-year view on the deployed network capacity and ONT/CPEs provided to customers avoids having to rip out relatively new equipment or finance the significant replacement of obsolete customer premise equipment that no longer can support the services required.

When we look at the economic drivers for fixed access, we can look at the capital cost of deploying a kilometer of fiber. This is particularly interesting if we are only interested in the fiber deployment itself and nothing else. Depending on the type of clutter, deployment, and labor cost occur. Maybe it is more interesting to bundle your investment into what is required to pass a household and what is required to connect a household (after it has been passed). Thus, we look at the Capex-per-home (or dwellings) passed and separate the Capex to connect an individual customer’s premise. It is important to realize that these Capex drivers are not just a single value but will depend on the household density depends on the type of area the deployment happens. We generally expect dense urban clutters to have a high dwelling density; thus, more households are covered (or passed) per km of fiber deployed. Dense-urban areas, however, may not necessarily hold the highest density of potential residential customers and hold less retail interest in the retail business. Generally, urban areas have higher household densities (including residential households) than sub-urban clutter. Rural areas are expected to have the lowest density and, thus, the most costly (on a household basis) to deploy.

Figure 17, just below, illustrates the basic economics of buried (as opposed to aerial) fiber for FTTH homes passed and FTTH homes connected. Apart from showing the intuitive economic logic, the cost per home passed or connected is driven by the household density (note: it’s one driver and fairly important but does not capture all the factors). This may serve as a base for rough assessments of the cost of fiber deployment in homes passed and homes connected as a function of household density. I have used data in the Fiber-to-the-Home Council Europe report of July 2012 (10 years old), “The Cost of Meeting Europe’s Network Needs” and have corrected for the European inflationary price increase since 2012 of ca. 14% and raised that to 20% to account for increased demand for FTTH related work by third parties. Then I checked this against some data points known to me (which do not coincide with the cities quoted in the chart). These data points relate to buried fiber, including the homes connected cost chart. Aerial fiber deployment (including home connected) would cost less than depicted here. Of course, some care should be taken in generalizing this to actual projects where proper knowledge of the local circumstances is preferred to the above.

Figure 17 The “chicken and egg” of connecting customers’ premises with fiber and providing them with 100s of Mbps up to Gbps broadband quality is that the fibers need to pass the home first, before the home can be connected. The cost of passing a premise (i.e., the home passed) and connecting a premise (home connected) should, for planning purposes, be split up. The cost of rolling out fiber to get homes-passed coverage is not surprisingly particularly sensitive to household density. We will have more households per unit area in urban areas compared to rural areas. Connecting a home is more sensitive to household density in deep rural areas where the distance from the main fiber line connection point to the household may be longer. The above cost curves are for buried fiber lines and are in 2021 prices.

Aerial fiber deployment would generally be less capital-intensive due to faster and easier deployment (less civil work, including permitting) using pre-existing (or newly built) poles. Not every country allows aerial deployment or even has the infrastructure (i.e., poles) available, which may be medium and low-voltage poles (e.g., for last-mile access). Some countries will have a policy allowing only buried fibers in the city or metropolitan areas and supporting pole infrastructure for aerial deployment in sub-urban and rural clutters. I have tried to illustrate this with Figure 18 below, where the pie charts show the aerial potential and share that may have to be assigned to buried fiber deployment.

Figure 18 above illustrates the amount of fiber coverage (i.e., in terms of homes passed) in Western European markets. The number for 2015 and 2020 is based on European Commission’s “Broadband Coverage in Europe 2020” (authored by Omdia et al.). The 2025 coverage number is my extrapolation of the 5-year trend leading up to 2020, considering the potential for aerial versus buried deployment. Aerial making accelerated deployment gains is more likely than in markets that only have buried fiber as a possibility, either because of regulation or lack of appropriate infrastructure for aerials. The only country that may be below 50% FTTH coverage in 2025 is Germany (i.e., DE), with a projected 39% of homes passed by 2025. Should Germany aim for 50% instead, they would have to do ca. 15 million households passed or, on average, 3 million a year from 2021 to 2025. Maximum Germany achieved in one year was in 2020, with ca. 1.4 million homes passed. The maximum any country in Europe has done in one year was France, with 2.9 million homes passed in 2018. However, France does allow for aerial fiber deployment outside major metropolitan areas.

Figure 19 above provides an overview across Western Europe for the last 5 years (2015 – 2020) of average annual household fiber deployment, the maximum done in one year in the previous 5 years, and the average required to achieve household coverage in 2025 shown above in Figure 13. For Germany (DE), the average deployment pace of 2.11 homes passed per year results in a coverage estimate of 39%. I don’t see any practical reasons for the UK, France, and Italy not to make the estimated household coverage by 2025, which may exceed my estimates.

From a deployment pace and Capex perspective, it is good to keep in mind that as time goes by, the deployment cost per household is likely to increase as household density reduces when the deployment moves from metropolitan areas toward suburban and rural. Thus, even if the deployment pace may reduce naturally for many countries in Figure 18 towards 2025, absolute Capex may not necessarily reduce accordingly.

In summary, the following topics would likely be on the Capex priority list;

  1. Continued fiber deployment to achieve household coverage. Based on Figure 16, at household (HH) densities above 500 per km2, the unit Capex for buried fiber should be below 900 Euro per HH passed with an average of 600 Euro per HH passed. Below 500 HH per km2, the cost increases rapidly towards 3,000 Euro per HH passed. The aerial deployment will result in substantially lower Capex, maybe with as much as 50% lower unit Capex.
  2. As customers subscribe, the fiber access cost associated with connecting homes (last-mile connectivity) will need to be considered. Figure 16 provides some guidance regarding the quantum-Euro range expected for buried fiber. Aerial-based connections may be somewhat cheaper.
  3. Life-cycle management (business-as-usual) investments, modernization investments, accommodating growth including new service and quality requirements (annual business as usual). Typically it would be upgrading OLT, ONTs, routers, and switches to support higher bandwidth requirements upgrading line cards (or interface cards), and moving from ≤100 Mbps to 1 Gbps and 10 Gbps. Many telcos will be considering upgrading their GPON (Gigabit Passive Optical Networks, 2.5 Gbps↓ / 1.2 Gbps↑) to provide XGPON (10 Gbps↓ / 2.5 Gbps↑) or even XGSPON services (10 Gbps↓ / 10 Gbps↑).
  4. Chinese supplier exposure and risks (i.e., political and regulatory enforcement) may be an issue in some Western European markets and require accelerated phase-out capital needs. In general, I don’t see fixed access infrastructure being a priority in this respect, given the strong focus on increasing household fiber coverage, which already takes up a lot of human and financial resources. However, this topic needs to be considered in case of obsolescence and thus would be a business case and performance-driven with a risk adjustment in dealing with Chinese suppliers at that point in time.

Fixed Access Capex KPIs: Capex share of Total, Capex per km, Number of HH passed and connected, Capex per HH passed, Capex per HH connected, Capex to Incremental Traffic, GPON, XGPON and XGSPON share of Capex and Households connected.

Whether actual and planned Capex is available or an analyst is modeling it, the above KPIs should be followed over an extended period. A single year does not tell much of a story.

Capex modeling comment: In a modeling exercise, I would use estimates for the telco’s household coverage plans as well as the expected household-connected sales projections. Hopefully, historical numbers would be available to the analyst that can be used to estimate the unit-Capex for a household passed and a household connected. You need to have an idea of where the telco is in terms of household density, and thus as time goes by, you may assume that the cost of deployment per household increases somewhat. For example, use Figure 17 to guide the scaling curve you need. The above-fixed access Capex KPIs should allow checking for inconsistencies in your model or, if you are reviewing a Capex plan, whether that Capex plan is self-consistent with the data provided.

If anyone would have doubted it, there is still much to do with fiber optical deployment in Western Europe. We still have around 100+ million homes to pass and a likely capital investment need of 100+ billion euros. Fiber deployment will remain a tremendously important investment area for the foreseeable future.

Figure 20 shows the remaining fiber coverage in homes passed based on 2020 actuals for urban and rural areas. In general, it is expected that once urban areas’ coverage has reached 80% to 90%, the further coverage-based rollout will reduce. Though, for attractive urban areas, overbuilt, that is, deploying fiber where there already are fibers deployed, is likely to continue.

Figure 21 illustrates the next 5 years’ weekly rollout to reach an 80% to 90% household coverage range by 2025. Further to the right, it shows an estimate of the remaining capital investment required to reach that 80% to 90% coverage range. This assessment is based on 2020 actuals from the European Commission’s “Broadband Coverage in Europe 2020” (authored by Omdia et al.); the weekly activity and Capex levels are thus from 2021 onwards.

In many Western European countries, the pace is expected to be increased considerably compared to the previous 5 years (i.e., 2015 – 2020). Even if the above figure may be over-optimistic, with respect to the goal of 2025, the European ambition for fiberizing European markets will impose a lot of pressure on speedy deployment.

IT investment levels are typically between 15% and 25% of Telecom Capex.

IT may be the most complex area to reach a consensus on concerning Capex. In my experience, it is also the area within a telco with the highest and most emotional discussion overhead within the operations and at a Board level. Just like everyone is far better at driving a car than the average driver, everyone is far better at IT than the IT experts and knows exactly what is wrong with IT and how to make IT much better and much faster, and much cheaper (if there ever was an area in telco-land where there are too many cooks).

Why is that the case? I tend to say that IT is much more “touchy-feely” than networks where most of the Capex can be estimated almost mathematically (and sufficiently complicated for non-technology folks to not bother with it too much … btw I tend to disagree with this from a system or architecture perspective). Of course, that is also not the whole truth.

IT designs, plans, develops (or builds), and operates all the business support systems that enable the business to sell to its customers, support its customers, and in general, keep the relationship with the customer throughout the customer life-cycle across all the products and services offered by the business irrespective of it being fixed or mobile or converged. IT has much more intense interactions with the business than any other technology department, whose purpose is to support the business in enabling its requirements.

Most of the IT Capex is related to people’s work, such as development, maintenance, and operations. Thus capitalized labor of external and internal labor is the main driver for IT Capex. The work relates to maintaining and improving existing services and products and developing new ones on the IT system landscape or IT stacks. In 2021, Western European telco Capex spending was about 20% of their total revenue. Out of that, 4±1 % or in the order of 10±3 billion Euro is spent on IT. With ca. 714 million fixed and mobile subscribers, this corresponds to an IT average spend of 14 Euros per telco customer in 2021. Best investment practices should aim at an IT Capex spend at or below 3% of revenue on average over 5 years (to avoid penalizing IT transformation programs). As a rule of thumb, if you do not have any details of internal cost structure (I bet you usually would not have that information), assume that the IT-related Opex has a similar quantum as Capex (you may compensate for GDP differences between markets). Thus, the total IT spend (Capex and Opex) would be in the order of 2×Capex, so the IT Spend to Revenue double the IT-related Capex to Revenue. While these considerations would give you an idea of the IT investment level and drill down a bit further into cost structure details, it is wise to keep in mind that it’s all a macro average, and the spread can be pretty significant. For example, two telcos with roughly the same number of customers, IT landscape, and complexity and have pretty different revenue levels (e.g., due to differences in ARPU that can be achieved in the particular market) may have comparable absolute IT spending levels but very different relative levels compared to the revenue. I also know of telcos with very low total IT spend to Revenue ITR (shareholder imposed), which had (and have) a horrid IT infrastructure performance with very extended outages (days) on billing and frequent instabilities all over its IT systems. Whatever might have been saved by imposing a dramatic reduction in the IT Capex (e.g., remember 10 million euros Capex reduction equivalent to 200 million euros value enhancement) was more than lost on inferior customer service and experience (including the inability to bill the customers).

You will find industry experts and pundits that expertly insist that your IT development spend is way too high or too low (although the latter is rare!). I recommend respectfully taking such banter seriously. Although try to understand what they are comparing with, what KPIs they are using, and whether it’s apples for apples and not with pineapples. In my experience, I would expect a mobile-only business to have a better IT spend level than a fixed-mobile telco, as a mobile IT landscape tends to be more modern and relatively simple compared to a fixed IT landscape. First, we often find more legacy (and I mean with a capital L) in the fixed IT landscape with much older services and products still being kept operational. The fixed IT landscape is highly customized, making transformation and modernization complex and costly. At least if old and older legacy products must remain operational. Another false friend in comparing one company IT spending with another’s is that the cost structure may be different. For example, it is worth understanding where OSS (Operational Support System) development is accounted for. Is it in the IT spend, or is it in the Network-side of things? Service platforms and Data Centers may be another difference where such spending may be with IT or Networks.

Figure 22 shows the helicopter view of a traditional telco IT architectural stack. Unless the telco is a true greenfield, it is a very normal state of affairs to have multiple co-existing stacks, which may have some degree of integration at various levels (sub-layers). Most fixed-mobile telcos remain with a high degree of IT architecture separation between their mobile and fixed business on a retail and B2B level. When approaching IT, investments never consider just one year. Understand their IT investment strategy in the immediate past (2-3 years prior) as well as how that fits with known and immediate future investments (2 – 3 years out).

Above, Figure 22 illustrates the typical layers and sub-layers in an IT stack. Every sub-layer may contain different applications, functionalities, and systems, all with an over-arching property of the sub-layer description. It is not uncommon for a telco to have multiple IT stacks serving different brands (e.g., value, premium, …) and products (e.g., mobile, fixed, converged) and business lines (e.g., consumer/retail, business-to-business, wholesale, …). Some layers may be consolidated across stacks, and others may be more fragmented. The most common division is between fixed and mobile product categories, as historically, the IT business support systems (BSS) as well as the operational support systems (OSS) were segregated and might even have been managed by two different IT departments (that kind of silliness is more historical albeit recent).

Figure 23 shows a typical fixed-mobile incumbent (i.e., anything not greenfield) multi-stack IT architecture and their most likely aspiration of aggressive integrated stack supporting a fixed-mobile conversion business. Out of experience, I am not a big fan of retail & B2B IT stack integration. It creates a lot of operational complexity and muddies the investment transparency and economics of particular B2B at the expense of the retail business

A typical IT landscape supporting fixed and mobile services may have quite a few IT stacks and a wide range of solutions for various products and services. It is not uncommon that a Fixed-Mobile telco would have several mobile brands (e.g., premium, value, …) and a separate (from an IT architecture perspective, at least) fixed brand. Then in addition, there may be differences between the retail (business-to-consumer, B2C) and the business-to-business (B2B) side of the telco, also being supported by separate stacks or different partitions of a stack. This is illustrated in Figure 20 above. In order for the telco business to become more efficient with respect to its IT landscape, including development, maintenance, and operational aspects of managing a complex IT infrastructure landscape, it should strive to consolidate stacks where it makes sense and not un-importantly along the business wish of convergence at least between fixed and mobile.

Figure 23 above illustrates an example of an IT stack harmonization activity long retail brands as well as Fixed and Mobile products as well as a separation of stacks into a retail and a business-to-business stack. It is, of course, possible to leverage some of the business logic and product synergies between B2C and B2B by harmonizing IT stacks across both business domains. However, in my experience, nothing great comes out of that, and more likely than not, you will penalize B2C by spending above and beyond value & investment attention on B2B. The B2B requirements tend to be significantly more complex to implement, their specifications change frequently (in line with their business customers’ demand), and the unit cost of development returns less unit revenue than the consumer part. Economically and from a value-consideration perspective, the telco needs to find an IT stack solution that is more in line with what B2B contributes to the valuation and fits its requirements. That may be a big challenge, particularly for minor players, as its business rarely justifies a standalone IT stack or developments. At least not a stack that is developed and maintained at the same high-quality level as a consumer stack. There is simply a mismatch in the B2B requirements, often having much higher quality and functionality requirements than the consumer part, and what it contributes to the business compared to, for example, B2C.

When I judge IT Capex, I care less about the absolute level of spend (within reason, of course) than what is practical to support within the given IT landscape the organization has been dealt with and, of course, the organization itself, including 3rd party support. Most systems will have development constraints and a natural order of how development can be executed. It will not matter how much money you are given or how many resources you throw at some problems; there will be an optimum amount of resources and time required to complete a task. This naturally leads to prioritization which may lead to disappointment of some stakeholders and projects that may not be prioritized to the degree they might feel entitled to.

When looking at IT capital spending and comparing one telco with another, it is worthwhile to take a 3- to 5-year time horizon, as telcos may be in different business and transformation cycles. A one-year comparison or benchmark may not be appropriate for understanding a given IT-spend journey and its operational and strategic rationale. Search for incidents (frequency and severity) that may indicate inappropriate spend prioritization or overall too little available IT budget.

The IT Capex budget would typically be split into (a) Consumer or retail part (i.e., B2C), (b) Business to Business and wholesale part, (c) IT technical part (optimization, modernization, cloudification, and transformations in general), and a (d) General and Administrative (G&A) part (e.g., Finance, HR, ..). Many IT-related projects, particularly of transformative nature, will run over multiple years (although if much more than 24 months, the risk of failure and monetary waste increases rapidly) and should be planned accordingly. For the business-driven demand (from the consumer, business, and wholesale), it makes sense to assign Capex proportional to the segment’s revenue and the customers those segments support and leverage any synergies in the development work required by the business units. For the IT part, capital spending should be assigned, ensuring that technical debt is manageable across the IT infrastructure and landscape and that efficiency gains arising from transformative projects (including landscape modernization) are delivered timely. In general, such IT projects promise efficiency in terms of more agile development possibilities (faster time to market), lower development and operational costs, and, last but not least, improved quality in terms of stability and reduced incidents. The G&A prioritizes finance projects and then HR and other corporate projects.

In summary, the following topics would likely be on the Capex priority list;

  1. Provide IT development support for business demand in the next business plan cycle (3 – 5 years with a strong emphasis on the year ahead). The allocation key should be close to the Revenue (or Ebitda) and customer contribution expected within the budget planning period. The development focus is on maintenance, (incremental) improvements to existing products/services, and new products/services required to make the business plans. In my experience, the initial demand tends to be 2 to 3 times higher than what a reasonable financial envelope would dictate (i.e., even considering what is possible to do within the natural limitations of the given IT landscape and organization) and what is ultimately agreed upon.
  2. Cloudification transformation journey moving away from the traditional monolithic IT platform and into a public, hybrid, or private cloud environment. The safest approach, in my opinion, is a “lift-and-shift” approach where existing functionality is developed in the cloud environment. After a successful migration from the traditional monolithic platform into the cloud environment, the next phase of the cloudification journey will be to move to a cloud-native framework should be embarked. This provides a very solid automation framework delivering additional efficiencies and improved stability and quality (e.g., reduction in incidents). Analysts should be aware that migrating to a (public) cloud environment may reduce the capitalization possibilities with the consequence that Capex may reduce in the forward budget planning, but this would be at the expense of increased Opex for the IT organization.
  3. Stack consolidation. Reducing the number of IT stacks generally lowers the IT Capex demand and improves development efficiency, stability, and quality. The trend is to focus on the harmonization efforts on the frontend (Portals and Outlets layer in Figure 14), the CRM layer (retiring legacy or older CRM solutions), and moving down the layers of the IT stack (see Figure 14) often touching the complex backend systems when they become obsolete providing an opportunity to migrate to a modern cloud-based solution (e.g., cloud billing).
  4. Modernization activities are not covered by cloudification investments or business requirements.
  5. Development support for Finance (e.g., ERP/SAP requirements), HR requirements, and other miscellaneous activities not captured above.
  6. Chinese suppliers are rarely an issue in Western European telecom’s IT landscape. However, if present in a telco’s IT environment, I would expect Capex has been allocated for phasing out that supplier urgently over the next 24 months (pending the complexity of such a transformation/migration program) due to strong political and regulatory pressures. Such an initiative may have a value-destructing impact as business-driven IT development (related to the specific system) might not be prioritized too highly during such a program and thus result in less ability to compete for the telco during a phase-out program.

IT Capex KPIs: IT share of Total Capex (if available, broken down into a Fixed and Mobile part), IT Capex to Revenue, ITR (IT total spend to Revenue), IT Capex per Customer, IT Capex per Employee, IT FTEs to Total FTEs.

Moreover, if available or being modeled, I would like to have an idea about how much of the IT Capex goes to investment categories such as (i) Maintain, (ii) Growth, and (iii) Transform. I will get worried if the majority of IT Capex over an extended period goes to the Growth category and little to Maintain and Transform. This indicates a telco that has deprioritized quality and ignores efficiency, resulting in the risk of value destruction over time (if such a trend were sustained). A telco with little Transform spend (again over an extended period) is a business that does not modernize (another word for sweating assets).

Capex modeling comment: when I am modeling IT and have little information available, I would first assume an IT Capex to Revenue ratio around 4% (mobile-only) to 6% (fixed-mobile operation) and check as I develop the other telco Capex components whether the IT Capex stays within 15% to 25%. Of course, keep an eye out for all the above IT Capex KPIs, as they provide a more holistic picture of how much confidence you can have in the Capex model.

Figure 24 Using New Street Research (total) Capex data for Western Europe and using the heuristics that IT spend typically would be 15% to 25% of the total Capex, we can estimate the most likely ranges of IT Capex to Revenue for the telecommunications business covered by NSR. For individual operations, we may also want to look at the time series of IT spending to revenue and compare that to any available intelligence (e.g., transformation intensive, M&A integration, business-as-usual, etc..)

Using the heuristic of the IT Capex being between 15% (1st quantile) and 25% (3rd quantile) of the total Capex, we can get an impression of how much individual Telcos invest in IT annually. The above chart shows such an estimate for 2021. I have the actual for several Western European Telcos, which agrees well with the above, typically a bit below the median.

Figure 25 having estimated the likely IT spend ranges for the various Western European telcos, allows us to estimate the IT spend per customer (using New Street Research data).

Similar to the IT Capex to Revenue, we can get an impression of what Telcos spend on IT Capex as it relates to their total mobile and fixed customer base. Again for Telcos in Western Europe (as well as outside), these ranges shown above do seem reasonable as the estimated range of where one would expect the IT spend.

Other, or miscellaneous, investments tend to be between 3% and 8% of the Telecom Capex.

When modeling a telco’s Capex, I find it very helpful to keep an “Other” or “Miscellaneous” Capex category for anything non-technology related. Modeling-wise, having a placeholder for items you don’t know about or may have forgotten is convenient. I typically start my models with 15% of all Capex. As my model matures, I should be able to reduce this to below 10% and preferably down to 5% (but I will accept 8% as a kind of good enough limit). I have had Capx review assignments where the Capex for future years had close to 20% in the “Miscellaneous.” If this “unspecified” Capex would not be included, the Capex to Revenue in the later years would drop substantially to a level that might not be deemed credible. In my experience, every planned Capex category will have a bit of “Other”-ness included as many smaller things require Capex but are difficult to mathematically derive a measure for. I tend to leave it if it is below 5% of a given Capex category. However, if it is substantial (>5%), it may reveal “sandbagging” or simply less maturity in the Capex planning and budget process.

Apart from a placeholder for stuff we don’t know, you will typically find Capex for shop refurbishment or modernization here, including office improvements and IT investments.

DE-AVERAGING THE TELECOM CAPEX TO FIXED AND MOBILE CONTRIBUTIONS.

There are similar heuristics to go deeper down into where the Capex should be spent, but that is a detail for another time.

Our first step is decomposing the total Capex into a fixed and a mobile component. We find that a multi-linear model including Total Capex, Mobile Customers, Mobile Service Revenue, Fixed Customers, and Fixed Service Revenues can account for 93% of the Capex trend. The multi-linear regression formula looks like the following;

with C = Capex, N = total customer count, R = service revenue, and α and β are the regression coefficient estimates from the multi-linear regression. The Capex model has been trained on 80% of the data (1,008 data points) chosen randomly and validated on the remainder (252 data points). All regression coefficients (4 in total) are statistically significant, with p-values well below a 95% confidence level.

Figure 26 above shows the Predicted Capex versus the Actual Capex. It illustrates that the predicted model agreed reasonably well with the actual Capex, which would also be expected based on the statistical KPIs resulting from the fit.

The Total is (obviously) available to us and therefore allows us to estimate both fixed and mobile Capex levels, by

The result of the fixed-mobile Capex decomposition is shown in Figure 26 below. Apart from being (reasonably) statistically sound, it is comforting that the trend in Capex for fixed and mobile seem to agree with what our intuition should be. The increase in mobile Capex (for Western Europe) over the last 5 years appears reasonable, given that 5G deployment commenced in early 2019. During the Covid lockdown from early 2020, fixed revenue was boosted by a massive shift in fixed broadband traffic (and voice) from the office to the individuals’ homes. Likewise, mobile service revenues have been in slow decline for years. Thus, the Capex increase due to 5G and reduced mobile service revenues ultimately leads to a relatively more significant increase in the mobile Capex to Revenue ratio.

Figure 27 illustrates the statistical modeling (by multi-linear regression), or decomposition, of the Total Capex as a function of Mobile Customers, Mobile Service Revenues, Fixed Customers, and Fixed Service Revenues, allowing to break up of the Capex into Fixed and Mobile components by decomposing the total Capex. The absolute Capex level is higher for fixed than what is found for mobile, with about a factor of 2 until 2021, when mobile Capex increases due to 5G investments in the mobile industry. It is found that the Mobile Capex has increased the most over the last 5 years (e.g., 5G deployment) while the service revenues have declined somewhat over the same period. This increased the Mobile Capex to Service Revenue ratio (note: based on Total Revenue, the ratio would be somewhat smaller, by ca. 17%). Source: Total Capex, Fixed, and Mobile Service revenues from New Street Research data for Western Europe. Note: The decomposition of the total Capex into Fixed and Mobile Capex is based on the author’s own statistical analysis and modeling. It is not a delivery of the New Street Research report.

CAN MOBILE-TRAFFIC GROWTH CONTINUE TO BE ACCOMMODATED CAPEX-WISE?

In my opinion, there has been much panic in our industry in the past about exhausting the cellular capacity of mobile networks and the imminent doom of our industry. A fear fueled by the exponential growth of user demand perceived inadequate spectrum amount and low spectral efficiency of the deployed cellular technologies, e.g., 3G-HSPA, classical passive single-in single-out antennas. Going back to the “hey-days” of 3G-HSPA, there was a fear that if cellular demand kept its growth rate, it would result in supply requirements going towards infinity and the required Capex likewise. So clearly an unsustainable business model for the mobile industry. Today, there is (in my opinion) no basis for such fears short or medium-term. With the increased fiberization of our society, where most homes will be connected to fiber within the next 5 – 10 years, cellular doomsday, in the sense of running out of capacity or needing infinite levels of Capex to sustain cellular demand, maybe a day never to come.

In Western Europe, the total mobile subscriber penetration was ca. 130% of the total population in 2021, with an excess of approximately 2.1+ mobile devices per subscriber. Mobile internet penetration was 76% of the total population in 2021 and is expected to reach 83% by 2025. In 2021, Europe’s average smartphone penetration rate was 77.6%, and it is projected to be around 84% by 2025. Also, by 2024±1, 50% of all connections in Western Europe are projected to be 5G connections. There are some expectations that around 2030, 6G might start being introduced in Western European markets. 2G and 3G will be increasingly phased out of the Western European mobile networks, and the spectrum will be repurposed for 4G and eventually 5G.

The above Figure 28 shows forecasted mobile users by their main mobile access technology. Source: based on the author’s forecast model relying on past technology diffusion trends for Western Europe and benchmarked against some WEU markets and other telco projections. See also 5G Standalone – European Demand & Expectations by Kim Larsen.

We may not see a complete phase-out of either older Gs, as observed in Figure 18. Due to a relatively large base of non-VOLTE (Voice-over-LTE) devices, mobile networks will have to support voice circuit-switched fallback to 2G or 3G. Furthermore, for the foreseeable future, it would be unlikely that all visiting roaming customers would have VOLTE-based devices. Furthermore, there might be legacy machine-2-machine businesses that would be prohibitively costly and complex to migrate from existing 2G or 3G networks to either LTE or 5G. All in all, ensure that 2G and 3G may remain with us for reasonably long.

Figure 29 above shows that mobile and fixed data traffic consumption is growing in totality and per-user level. On average mobile traffic grew faster than fixed from 2015 to 2021. A trend that is expected to continue with the introduction of 5G. Although the total traffic growth rate is slowing down somewhat over the period, on a per-user basis (mobile as well as fixed), the consumptive growth rate has remained stable.

Since the early days of 3G-HSPA (High-Speed Packet Access) radio access, investors and telco businesses have been worried that there would be an end to how much demand could be supported in our cellular networks. The “fear” is often triggered by seeing the exponential growth trend of total traffic or of the usage per customer (to be honest, that fear has not been made smaller by technology folks “panicking” as well).

Let us look at the numbers for 2021 as they are reported in the Cisco VNI report. The total mobile data traffic was in the order of 4 Exabytes (4 Billion gigabytes, GB), more than 5.5× the level of 2016. It is more than 600 million times the average mobile data consumption of 6.5 GB per month per customer (in 2021). Compare this with the Western European population of ca. 200 million. While big numbers, the 6.5 GB per month per customer is insignificant. Assuming that most of this volume comes from video streaming at an optimum speed of 3 – 5 Mbps (good enough for HD video stream), the 6.5 GB translates into approx. 3 – 5 hours of video streaming over a month.

The above Figure 30 Illustrates a 24-hour workday total data demand on the mobile network infrastructure. A weekend profile would be more flattish. We spend at least 12 hours in our home, ca. 7 hours at work (including school), and a maximum of 5 hours (~20%) commuting, shopping, and otherwise being away from our home or workplace. Previous studies of mobile traffic load have shown that 80% of a consumer’s mobile demand falls in 3 main radio node sites around the home and workplace. The remaining 20% tends to be much more mobile-like in the sense of being spread out over many different radio-node sites.

Daily we have an average of ca. 215 Megabytes per day (if spread equally over the month), corresponding to 6 – 10 minutes of video streaming. The average length of a YouTube was ca. 4.4 minutes. In Western Europe, consumers spend an average of 2.4 hours per day on the internet with their smartphones (having younger children, I am surprised it is not more than that). However, these 2.4 hours are not necessarily network-active in the sense of continuously demanding network resources. In fact, most consumers will be active somewhere between 8:00 to around 22:00, after which network demand reduces sharply. Thus, we have 14 hours of user busy time, and within this time, a Western European consumer would spend 2.4 hours cumulated over the day (or ca. 17% of the active time).

Figure 31 above illustrates (based on actual observed trends) how 5 million mobile users distribute across a mobile network of 5,000 sites (or radio nodes) and 15,000 sectors (typically 3 sectors = 1 site). Typically, user and traffic distributions tend to be log-norm-like with long tails. In the example above, we have in the busy hour a median value of ca. 80 users attached to a sector, with 15 being active (i.e., loading the network) in the busy hour, demanding a maximum of ca. 5 GB (per sector) or an average of ca. 330 MB per active user in the radio sector over that sector’s relevant busy hour.

Typically, 2 limits, with a high degree of inter-dependency, would allegedly hit the cellular businesses rendering profitable growth difficult at some point in the future. The first limit is a practical technology limit on how much capacity a radio access system can supply. As we will see a bit later, this will depend on the operator’s frequency spectrum position (deployed, not what might be on the shelf), the number of sites (site density), the installed antenna technology, and its effective spectral efficiency. The second (inter-dependent) limit is an economic limit. The incremental Capex that telcos would need to commit to sustaining the demand at a given quality level would become highly unprofitable, rendering further cellular business uneconomical.

From a Capex perspective, the cellular access part drives a considerable amount of the mobile investment demand. Together with the supporting transport, such as fronthaul, backhaul, aggregation, and core transport, the capital investment share is typically 50% or higher. This is without including the spectrum frequencies required to offer the cellular service. Such are usually acquired by local frequency spectrum auctions and amount to substantial investment levels.

In the following, the focus will be on cellular access.

Cellular Demand.

Before discussing the cellular supply side of things, let us first explore the demand side from the view of a helicopter. Demand is created by users (N) of the cellular services offered by telcos. Users can be human or non-human such as things in general or more specific machines. Each user has a particular demand that, in an aggregated way, can be represented by the average demand in Bytes per User (d). Thus, we can then identify two growth drivers. One from adding new users (ΔN) to our cellular network and another from the incremental change in demand per user (ΔN) as time goes by.

It should be noted that the incremental change in demand or users might not per se be a net increase. Still, it could also be a net decrease, either because the cellular networks have reached the maximum possible level of capacity (or quality) that results in users either reducing their demand or “ churning” from those networks or that an alternative to today’s commercial cellular network triggers abandonment as high-demand users migrate to that alternative — leading both to a reduction in cellular users and the average demand per user. For example, a near-100% Fiber-to-the-Home coverage with supporting WiFi could be a reason for users to abandon cellular networks, at least in an indoor environment, which would reduce between 60 to 80% of present-day cellular data demand. This last (hypothetical) is not an issue for today’s cellular networks and telco businesses.

Of course, this can easily be broken down into many more drivers and details, e.g., technology diffusion or adaptation, the rate of users moving from one access technology to another (e.g., 3G→4G, 4G→5G, 5G→FTTH+WiFi), improved network & user device capabilities (better coverage, higher speeds, lower latency, bigger display size, device chip generation), new cellular service adaptation (e.g., TV streaming, VR, AR, …), etc.…

However, what is often forgotten is that the data volume of consumptive demand (in Byte) is not the main direct driver for network demand and, thus, not for the required investment level. A gross volumetric demand can be caused by various gross throughput demands (bits per second). The throughput demanded in the busiest hour ( or ) is the direct driver of network load, and thus, network investments, the volumetric demand, is a manifestation of that throughput demand.

With being the number of active users in a given radio cell at the time-instant of unit t taken within a day.  is the Bytes consumed in a time instant (e.g., typically a second); thus, 8  gives us the bits per time unit (or bits/sec), which is throughput consumed. Sum over all the cells’ instant throughput ( bits/sec) in the same instant and take the maximum across. For example, a day provides the busiest hour throughput for the whole network. Each radio cell drives its capacity provision and supply (in bits/sec) and the investments required to provide that demanded capacity in the air interface and front- and back-haul.

For example, if n = 6 active (concurrent) users, each consuming on average  = 0.625 Mega Bytes per second (5 Megabits per second, Mbps), the typical requirement for a YouTube stream with an HD 1080p resolution, our radio access network in that cell would experience a demanded load of 30 Mbps (i.e., 6×5 Mbps). Of course, provided that the given cell has sufficient capacity to deliver what is demanded. A 4G cellular system, without any special antenna technology, e.g., Single-in-Single-out (SiSo) classical antenna and not the more modern Multiple-in-Multiple-out (MiMo) antenna, can be expected to deliver ca. 1.5 Mbps/MHz per cell. Thus, we would need at least 20 MHz spectrum to provide for 6 concurrent users, each demanding 5 Mbps. With a simple 2T2R MiMo antenna system, we could support about 8 simultaneous users under the same conditions. A 33% increase in what our system can handle without such an antenna. As mobile operators implement increasingly sophisticated antenna systems (i.e., higher-order MiMo systems) and move to 5G, a leapfrog in the handling capacity and quality will occur.

Figure 32 Is the sky the limit to demand? Ultimately, the limit will come from the practical and economic limits to how much can be supplied at the cellular level (e.g., spectral bandwidth, antenna technology, and software features …). Quality will reduce as the supply limit is reached, resulting in demand adaptation, hopefully settling at a demand-supply (metastable) equilibrium.

Cellular planners have many heuristics to work with that together trigger when a given radio cell would be required to be expanded to provide more capacity, which can be provided by software (licenses), hardware (expansion/replacement), civil works (sectorization/cell splits) and geographical (cell split) means. Going northbound, up from the edge of the radio network up through the transmission chain, such as fronthaul, back, aggregation, and core transport network, may require additional investments in expanding the supplied demand at a given load level.

As discussed, mobile access and transport together can easily make up more than half of a mobile capital budget’s planned and budgeted Capex.

So, to know whether the demand triggers new expansions and thus capital demand as well as the resulting operational expenses (Opex), we really need to look at the supply side. That is what our current mobile network can offer. When it cannot provide a targeted level of quality, how much capacity do we have to add to the network to be on a given level of service quality?

Cellular Supply.

Cellular capacity in units of throughput () given in bits per second, the basic building block of quality, is relatively easy to estimate. The cellular throughput (per unit cell) is provided by the amount of committed frequency spectrum to the air interface, what your radio access network and antenna support are, multiplied by the so-called spectral efficiency in bits per Hz per cell. The spectral efficiency depends on the antenna technology and the underlying software implementation of signal processing schemes enabling the details of receiving and sending signals over the air interface.

$T_{supply}$ can be written as follows;

With Mbps being megabits (a million bits) per second and MHz being Mega Herz.

For example, if we have a site that covers 3 cells (or sectors) with a deployed 100 MHz @ 3.6GHz (B) on a 32T32R advanced antenna system (AAS) with an effective downlink (i.e., from the antenna to user), spectral efficiency of ca. 20 Mbps/MHz/cell (i.e., ), we should expect to have a cell throughput on average at 1,000 Mbps (1 Gbps).

The capacity supply formula can be applied to the cell-level consideration providing sizing and thus investment guidance as we move northbound up the mobile network and traffic aggregates and concentrates towards the core and connections points to the external internet.

From the demand planning (e.g., number of customers, types of services sold, etc..), that would typically come from the Marketing and Sales department within the telco company, the technical team can translate those plans into a network demand and then calculate what they would need to do to cope with the customer demand within an agreed level of quality.

In Figure 33 above, operators provide cellular capacity by deploying their spectral assets on an appropriate antenna type and system-level radio access network hardware and software. Competition can arise from a superior spectrum position (balanced across low, medium, and high-frequency bands), better or more aggressive antenna technology, and utilizing their radio access supplier(s)’ features (e.g., signal processing schemes). Usually, the least economical option will be densifying the operator’s site grid where needed (on a macro or micro level).

Figure 34 above shows the various options available to the operator to create more capacity and quality. In terms of competitive edge, more spectrum than competitors provided it is being used and is balanced across low, medium, and high bands, provides the surest path to becoming the best network in a given market, and be difficult to economically copy by operators with substantially less spectrum. Their options would be compensating for the spectrum deficit by building more sites and deploying more aggressive antenna technologies. The last one is relatively easy to follow by anyone and may only provide some respite temporarily.  

An average mobile network in Western Europe has ca. 270 MHz spectrum (60 MHz low-band below 1800 and 210 MHz medium-band below 5 GHz) distributed over an average of 7 cellular frequency bands. It is rare to see all bands deployed in actual deployments and not uniformly across a complete network. The amount of spectrum deployed should match demand density; thus, more spectrum is typically deployed in urban areas than in rural ones. In demand-first-driven strategies, the frequency bands will be deployed based on actual demand that would typically not require all bands to be deployed. This is opposed to MNOs that focus on high quality, where demand is less important and where typically, most bands would be deployed extensively across their networks. The demand-first-driven strategy tends to be the most economically efficient strategy as long as the resulting cellular quality is market-competitive and customers are sufficiently satisfied.

In terms of downlink spectral capacity, we have an average of 155 MHz or 63 MHz, excluding the C-band contribution. Overall, this allows for a downlink supply of a minimum of 40 GB per hour (assuming low effective spectral efficiency, little advanced antenna technology deployed, and not all medium-band being utilized, e.g., C-Band and 2.5 GHz). Out of the 210 MHz mid-band spectrum, 92 MHz falls in the 3.X GHz (C-band) range and is thus still very much in the process of being deployed for 5G (as of June 2022). The C-band has, on average, increased the spectral capacity of Western European telcos by 50+% and, with its very high suitability for deployment together with massive MiMo and advanced antenna systems, effectively more than doubled the total cellular capacity and quality compared to pre-C-band deployment (using a 64T64R massive MiMo as a reference with today’s effective spectral efficiency … it will be even better as time goes by).

Figure 35 (above) shows the latest Ookla and OpenSignal DL speed benchmarks for Western Europe MNOs (light blue circles), and comparing this with their spectrum holdings below 3.x GHz indicates that there may be a lot of unexploited cellular capacity and quality to be unleashed in the future. Although, it would not be for free and likely require substantial additional Capex if deemed necessary. The ‘Expected DL Mbps’ (orange solid line, *) assumes the simplest antenna setup (e.g., classical SiSo antennas) and that all bands are fully used. On average, MNOs above the benchmark line have more advanced antenna setups (higher-order antennas) and fully (or close to) spectrum deployment. MNOs below the benchmark line likely have spectrum assets that have not been fully deployed yet and (or) “under-prioritized” their antenna technology infrastructure. The DL spectrum holding excludes C- and mmWave spectrum. Note:  There was a mistake in the original chart published on LinkedIn as the data was depicted against the total spectrum holding (DL+UL) and not only DL. Data: 54 Western European telcos.

Figure 35 illustrates the Western European cellular performance across MNOs, as measured by DL speed in Mbps, and compares this with the theoretical estimate of the performance they could have if all DL spectrum (not considering C-band, 3.x GHz) in their portfolio had been deployed at a fairly simple antenna setup (mainly SiSo and some 2T2R MiMo) with an effective spectral efficiency of 0.85 Mbps per MHz. It is good to point out that this is expected of 3G HSPA without MiMo. We observe that 21 telcos are above the solid (orange) line, and 33 have an actual average measured performance that is substantially below the line in many cases. Being above the line indicates that most spectrum has been deployed consistently across the network, and more advanced antennas, e.g., higher-order MiMo, are in use. Being below the line does (of course) not mean that networks are badly planned or not appropriately optimized. Not at all. Choices are always made in designing a cellular network. Often dictated by the economic reality of a given operator, geographical demand distribution, clutter particularities, or the modernization cycle an operator may be in. The most obvious reasons for why some networks are operating well under the solid line are; (1) Not all spectrum is being used everywhere (less in rural and more in urban clutter), (2) Rural configurations are simpler and thus provide less performance than urban sites. We have (in general) more traffic demand in urban areas than in rural. Unless a rural area turns seasonally touristic, e.g., lake Balaton in Hungary in the summer … It is simply a good technology planning methodology to prioritize demand in Capex planning, and it makes very good economic sense (3) Many incumbent mobile networks have a fundamental grid based on (GSM) 900MHz and later in-filled for (UMTS) 2100MHz…which typically would have less site density than networks based on (DCS) 1800MHz. However, site density differences between competing networks have been increasingly leveled out and are no longer a big issue in Western Europe (at least).

Overall, I see this as excellent news. For most mobile operators, the spectrum portfolio and the available spectrum bandwidth are not limiting factors in coping with demanded capacity and quality. Operators have many network & technology levers to work with to increase both quality and capacity for their customers. Of course, subject to a willingness to prioritize their Capex accordingly.

A mobile operator has few options to supply cellular capacity and quality demanded by its customer base.

  • Acquire more spectrum bandwidth by buying in an auction, buying from 3rd party (including M&A), asymmetric sharing, leasing, or trading (if regulatory permissible).
  • Deploy a better (spectral efficient) radio access technology, e.g., (2G, 3G) → (4G, 5G) or/and 4G → 5G, etc. Benefits will only be seen once a critical mass of customer terminal equipment supporting that new technology has been reached on the network (e.g., ≥20%).
  • Upgrade antenna technology infrastructure from lower-order passive antennas to higher-order active antenna systems. In the same category would be to ensure that smart, efficient signal processing schemes are being used on the air interface.
  • Building a denser cellular network where capacity demand dictates or coverage does not support the optimum use of higher frequency bands (e.g., 3.x GHz or higher).
  • Small cell deployment in areas where macro-cellular built-out is no longer possible or prohibitively costly. Though small cells scale poorly with respect to economics and maybe really the last resort.

Sectorization with higher-frequency massive-MiMo may be an alternative to small-cell and macro-cellular additions. However, sectorization requires that it is possible civil-engineering wise (e.g., construction) re: structural stability, permissible by the landlord/towerco and finally economic compared to a new site built. Adding more than the usual 3-sectors to a site would further boost site spectral efficiency as more antennas are added.

Acquiring more spectrum requires that such spectrum is available either by a regulatory offering (public auction, public beauty contest) or via alternative means such as 3rd party trading, leasing, asymmetric sharing, or by acquiring an MNO (in the market) with spectrum. In Western Europe, the average cost of spectrum is in the ballpark of 100 million Euro per 10 million population per 20 MHz low-band or 100 MHz medium bands. Within the European Union, recent auctions provide a 20-year usage-rights period before the spectrum would have to be re-auctioned. This policy is very different from, for example, in the USA, where spectrum rights are bought and ownership secured in perpetuity (sometimes conditioned on certain conditions being met). For Western Europe, apart from the mmWave spectrum, in the foreseeable future, there will not be many new spectrum acquisition opportunities in the public domain.

This leaves mobile operators with other options listed above. Re-farming spectrum away from legacy technology (e.g., 2G or 3G) in support of another more spectral efficient access technology (e.g., 4G and 5G) is possibly the most straightforward choice. In general, it is the least costly choice provided that more modern options can support the very few customers left. For either retiring 2G or 3G, operators need to be aware that as long as not all terminal equipment support Voice-over-LTE (VoLTE), they need to keep either 2G or 3G (but not both) for 4G circuit-switched fallback (to 2G or 3G) for legacy voice services. The technologist should be prepared for substantial pushback from the retail and wholesale business, as closing down a legacy technology may lead to significant churn in that legacy customer base. Although, in absolute terms, the churn exposure should be much smaller than the overall customer base. Otherwise, it will not make sense to retire the legacy technology in the first place. Suppose the spectral re-farming is towards a new technology (e.g., 5G). In that case, immediate benefits may not occur before a critical mass of capable devices is making use of the re-farmed spectrum. The Capex impact of spectral re-farming tends to be minor, with possibly some licensing costs associated with net savings from retiring the legacy. Most radio departments within mobile operators, supplier experts, and managed service providers have gained much experience in this area over the last 5 – 7 years.

Another venue that should be taken is upgrading or modernizing the radio access network with more capable antenna infrastructure, such as higher-order massive MiMo antenna systems. As has been pointed out by Prof. Emil Björnson also, the available signal processing schemes (e.g., for channel estimation, pre-coding, and combining) will be essential for the ultimate gain that can be achieved. This will result in a leapfrog increase in spectral efficiency. Thus, directly boosting air-interface capacity and the quality that the mobile customer can enjoy. If we take a 20-year period, this activity is likely to result in a capital demand in the order of 100 million euros for every 1,000 sites being modernized and assumes a modernization (or obsolescence) cycle of 7 years. In other words, within the next 20 years, a mobile operator will have undergone at least 3 antenna-system modernization cycles. It is important to emphasize that this does not (entirely) cover the likely introduction of 6G during the 20 years. Operators face two main risks in their investment strategy. One risk is that they take a short-term look at their capital investments and customer demand projections. As a result, they may invest in insufficient infrastructure solutions to meet future demands, forcing accelerated write-offs and re-investments. The second significant risk is that the operator invests too aggressively upfront in what appears to be the best solution today to find substantially better and more efficient solutions in the near future that more cautious competitive operators could deploy and achieve a substantially higher quality and investment efficiency. Given the lack of technology maturity and the very high pace of innovation in advanced antenna systems, the right timing is crucial but not straightforward.

Last and maybe least, the operator can choose to densify its cellular grid by adding one or more macro-cellular sites or adding small cells across existing macro-cellular coverage. Before it is possible to build a new site or site, the operator or the serving towerco would need to identify suitable locations and subsequently obtain a permit to establish the new site or site. In urban areas, which typically have the highest macro-site densities, getting a new permit may be very time-consuming and with a relatively high likelihood of not being granted by the municipality. Small cells may be easier to deploy in urban environments than in macro sites. For operators making use of towerco to provide the passive site infrastructure, the cost of permitting and building the site and materials (e.g., steel and concrete) is a recurring operational expense rather than a Capex charge. Of course, active equipment remains a Capex item for the relevant mobile operator.

The conclusion I make above is largely consistent with the conclusions made by New Street Research in their piece “European 5G deep-dive” (July 2021). There is plenty of unexploited spectrum with the European operators and even more opportunity to migrate to more capable antenna systems, such as massive-MiMo and active advanced antenna systems. There are also above 3GHz, other spectrum opportunities without having to think about millimeter Wave spectrum and 5G deployment in the high-frequency spectrum range.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. There should be no doubt that without the support of Russell Waller (New Street Research), this blog would not have been possible. Thank you so much for providing much of the data that lays the ground for much of the Capex analysis in this article. Of course, a lot of thanks go out to my former Technology and Network Economics colleagues, who have been a source of inspiration and knowledge. I cannot get away with acknowledging Maurice Ketel (who for many years let my Technology Economics Unit in Deutsche Telekom, I respect him above and beyond), Paul Borker, David Haszeldine, Remek Prokopik, Michael Dueser, Gudrun Bobzin, as well as many, many other industry colleagues who have contributed with valuable insights, discussions & comments throughout the years. Many thanks to Paul Zwaan for a lot of inspiration, insights, and discussions around IT Architecture.

Without executive leadership’s belief in the importance of high-quality techno-financial models, I have no doubt that I would not have been able to build up the experience I have in this field. I am forever thankful, for the trust and for making my professional life super interesting and not just a little fun, to Mads Rasmussen, Bruno Jacobfeuerborn, Hamid Akhavan, Jim Burke, Joachim Horn, and last but certainly not least, Thorsten Langheim.

FURTHER READING.

  1. Kim Kyllesbech Larsen, “5G Standalone European Demand Expectations (Part I).”, (January, 2022).
  2. Kim Kyllesbech Larsen, “RAN Unleashed … Strategies for being the best (or the worst) cellular network (Part III).”, (January, 2022).
  3. Tom Copeland, Tim Koller, and Jack Murrin, “Valuation”, John Wiley & Sons, (2000). I regard this as my “bible” when it comes to understanding enterprise valuation. There are obviously many finance books on valuation (I have 10 on my bookshelf). Copeland’s book is the best imo.
  4. Stefan Rommer, Peter Hedman, Magnus Olsson, Lars Frid, Shabnam Sultana, and Catherine Mulligan, “5G Core Networks”, Academic Press, (2020, 1st edition). Good account for what a 5G Core Network entails.
  5. Jia Shen, Zhongda Du, Zhi Zhang, Ning Yang and Hai Tang, “5G NR and enhancements”, Elsevier (2022, 1st edition). Very good and solid account of what 5G New Radio (NR) is about and the considerations around it.
  6. Wim Rouwet, “Open Radio Access Network (O-RAN) Systems Architecture and Design”, Academic Press, (2022). One of the best books on Open Radio Access Network architecture and design (honestly, there are not that many books on this topic yet). I like that the author, at least as an introduction makes the material reasonably accessible to even non-experts (which tbh is also badly needed).
  7. Strand Consult, “OpenRAN and Security: A Literature Review”, (June, 2022). Excellent insights into the O-RAN maturity challenges. This report focuses on the many issues around open source software-based development that is a major part of O-RAN and some deep concerns around what that may mean for security if what should be regarded as critical infrastructure. I warmly recommend their “Debunking 25 Myths of OpenRAN”.
  8. Hwaiyu Geng P.E., “Data Center Handbook”, Wiley (2021, 2nd edition). I have several older books on the topic that I have used for my models. This one brings the topic of data center design up to date. Also includes the topic of Cloud and Edge computing. Good part on Data Center financial analysis. 
  9. James Farmer, Brian Lane, Kevin Bourgm Weyl Wang, “FTTx Networks, Technology Implementation, and Operations”, Elsevier, (2017, 1st edition). It has some books covering FTTx deployment, GPON, and other alternative fiber technologies. I like this one in particular as it covers hands-on topics as well as basic technology foundations.
  10. Tower companies overview, “Top-12 Global 5G Cell Tower Companies 2021”, (Nov. 2021). A good overview of international tower companies with a meaningful footprint in Europe.
  11. New Street Research, “European 5G deep-dive”, (July, 2021).
  12. Prof. Emil Björnson, https://ebjornson.com/research/ and references therein. Please take a look at many of Prof. Björnson video presentations (e.g., many brilliant YouTube presentations that are fairly assessable).

RAN Unleashed … Strategies for being the best (or the worst) cellular network (Part III).

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I have been spending my holiday break this year (December 2021) updating my dataset on Western Europe Mobile Operators, comprising 58+ mobile operators in 16 major Western European markets, focusing on spectrum positions, market dynamics, technology diffusion (i.e., customer migration to 5G), advanced antenna strategies, (modeled) investment levels and last but not least answering the question: what makes a cellular network the best in a given market or the world. What are the critical ingredients for an award-winning mobile network?

An award-winning cellular network, the best network, also provides its customers with a superior experience, the best network experience possible in a given market.

I am fascinated by the many reasons and stories we tell ourselves (and others) why this or that cellular network is the best. The story may differ whether you are an operator, a network supplier, or an analyst covering the industry. I have had the privileged to lead a mobile network (T-Mobile Netherlands) that won the Umlaut best mobile network award in The Netherlands since 2016 (5 consecutive times) and even scored the highest amount of points in the world in 2019 and 2020/2021. So, I guess it would make me a sort of “authority” on winning best network awards? (=sarcasm).

In my opinion and experience, a cellular operator has a much better than fair chance at having the best mobile network, compared to its competition, with access to the most extensive active spectrum portfolio, across all relevant cellular bands, implemented on a better (or best) antenna technology (on average) situated on a superior network footprint (e.g., more sites).

For T-Mobile Netherlands, firstly, we have the largest spectrum portfolio (260 MHz) compared to KPN (205 MHz) and Vodafone (215 MHz). The spectrum advantage of T-Mobile, as shown above, is both in low-band (< 1800 MHz) as well as mid-band range (> 1500 MHz). Secondly, as we started out back in 1998, our cell site grid was based on 1800 MHz, requiring a denser cell site grid (thus, more sites required) than the networks based on 900 MHz of the two Dutch incumbent operators, KPN and Vodafone. Therefore, T-Mobile ended up with more cell sites than our competition. We maintained the site advantage even after the industry’s cell grid densification needs of UMTS at 2100 MHz (back in the early 2000s). Our two very successful mergers have also helped our site portfolio, back in 2007 acquiring and merging with Orange NL and in 2019 merging with Tele2 NL.

The number of sites (or cells) matter for coverage, capacity, and overall customer experience. Thirdly, T-Mobile was also first in deploying advanced antenna systems in the Dutch market (e.g., aggressive use of higher-order MiMo antennas) across many of our frequency bands and cell sites. Our antenna strategy has allowed for a high effective spectral efficiency (across our network). Thus, we could (and can) handle more bits per second in our network than our competition.

Moreover, over the last 3 years, T-Mobile has undergone (passive) site modernization that has improved coverage and quality for our customers. This last point is not surprising since the original network was built based on a single 1800 MHz frequency, and since 1998 we have added 7 additional bands (from 700 MHz to 2.5 GHz) that need to be considered in the passive site optimization. Of course, as site modernization is ongoing, an operator (like T-Mobile) also should consider the impact of future bands that may be required (e.g., 3.x GHz). Optimize subject to the past as well as the future spectrum outlook. Last but not least, we at T-Mobile have been blessed with a world-class engineering team that has been instrumental in squeezing out continuous improvements of our cellular network over the last 6 years.

So, suppose you have 25% less spectrum than a competitor. In that case, you either need to compensate by building 25% more cells (very costly & time-consuming), deploying better antennas with a 25% better effective spectral efficiency (limited, costly & relatively easy to copy/match), or a combination of both (expensive & time-consuming). The most challenging driver to copy for network superiority is the amount of spectrum. A competitor only compensates by building more sites, deploying better antenna technology, and over decades to try to equalize spectrum position is subsequent spectrum auctions (e.g., valid for Europe, not so for the USA where acquired spectrum usually is owned in perpetuity).

T-Mobile has consistently won the best mobile network award over the last 6 years (and 5 consecutive times) due to these 3 multiplying core dimensions (i.e., spectrum × antenna technology × sites) and our world-class leading engineering team.

THE MAGIC RECIPE FOR CELLULAR PERFORMANCE.

We can formalize the above network heuristics in the following key (very beautiful IMO) formula for cellular network capacity measured in throughput (bits per second);

It is actually that simple. Cellular capacity is made as simple as possible, dependent on three basic elements, but not more straightforward. Maybe, super clear, though only active spectrum counts. Any spectrum not deployed is an opportunity for a competitor to gain network leadership on you.

If an operator has a superior spectrum position and everything else is equal (i.e., antenna technology & the number of sites), that operator should be unbeatable in its market.

There are some caveats, though. In an overloaded (congested) cellular network, performance would decrease, and superior network performance would be unlikely to be ensured compared to competitors not experiencing such congestion. Furthermore, spectrum superiority must be across the depth of the market-relevant cellular frequencies (i.e., 600 MHz – 3.x GHz and higher). In other words, if a cellular operator “only” has to work with, for example, 100 MHz @ 3.5GHz, it is unlikely that this would guarantee a superior network performance across a market (country) compared to a much better balance spectrum portfolio.

The option space any operator has is to consider the following across the three key network quality dimensions;

Let us look at the hypothetical Western European country Mediana. Mediana, with a population of 25 million, has 3 mobile operators each have 8 cellular frequency bands, incumbent Winky has a total cellular bandwidth of 270 MHz, Dipsy has 220 MHz, and Po has 320 MHz (top their initial weaker spectrum position through acquisitions). Apart from having the most robust spectrum portfolio, Po also has more cell sites than any other in the market (10,000) and keeps winning the best network award. Winky, being the incumbent, is not happy about this situation. No new spectrum opportunities will become available in the next 10 years. Winky’s cellular network, based initially on 900MHz but densified over time, has about 20% fewer sites than Po. Po and Winky’s deployed state of antenna technology is comparable.

What can Winky do to gain network leadership? Winky has assessed that Po has ca. 20% stronger spectrum position than they, state of antenna technology is comparable, and they (Po) have ca. 20% more sites. Using the above formula, Winky estimates that Po’s have 44% more raw cellular network quality available compared to their own capability. Winky’s commenced a network modernization program that adds another 500 new sites and significantly improves their antenna technology. After this modernization program, Winky has decreased its site deficit to having 10% fewer sites than Po and almost 60% better antenna technology capability than Po. Overall, using the above network quality formula, Winky has changed their network position to a lead over Po with ca. 18%. In theory, it should have an excellent chance to capture the best network award.

Of course, Po could simply follow and deploy the same antenna technology as Winky and would easily overtake Winky’s position due to its superior spectrum position (that Winky cannot beat the next 10 to 15 years at least).

In economic terms, it may be tempting to conclude that Winky has avoided 625 Million Euro in spectrum fees by possessing 50 MHz less than Po (i.e., median spectrum fee in Mediana is 0.50 Euro per MHz per pop times the avoided 50 MHz times the population of Mediana 25 Million pops) and that for sure should allow Winky to make a lot of network (and market) investments to gain network leadership. By adding more sites, assuming it is possible to do where they are needed and invest in better antenna technology. However, do the math with realistic prices and costs incurred over a 10 to 15 year period (i.e., until the next spectrum opportunity). You may be more likely to find a higher total cost for Winky than the spectrum fee avoidance. Also, the strategy of Winky is easy to copy and overtake in the next modernization cycle of Po.

Is there any value for operators engaging in such the best network equivalent of a “nuclear arms” race? That interesting question is for another article. Though the answer (spoiler alert) is (maybe) not so black and white as one may think.

An operator can compensate for a weaker spectrum position by adding more cell sites and deploying better antenna technologies.

A superior spectrum portfolio is not an entitlement. Still, an opportunity to become the sustainable best network in a given market (for the duration that spectrum is available to the operator, e.g., 10 – 15 years in Europe at least).

WESTERN EUROPE SPECTRUM POSITIONS.

A cellular operator’s spectrum position is an important prerequisite for superior performance and customer experience. If an operator has the highest amount of spectrum (well balanced over low, mid, and high-frequency bands), it will have a powerful position to become the best network in that given market. Using Spectrum Monitor’s Global Mobile Frequency database (last updated May 2021), I analyzed the spectrum position of a total of 58 cellular operators in 16 Western European markets. The result is shown below as (a) Total spectrum position, (b) Low-band spectrum position covering spectrum below and including 1500 MHz (SDL band), and (c) Mid-band spectrum covering the spectrum above 1500 MHz (SDL band). For clarity, I include the 3.X GHz (C-band) as mid-band and do not include any mmWave (n257 band) positions (anyway would be high band, obviously).

4 operators are in a category by themselves with 400+ MHz of total cellular bandwidth in their spectrum portfolios; A1 (Austria), TDC (Denmark), Cosmote (Greece), and Swisscom (Switzerland). TDC and Swisscom have incredibly strong low-band and mid-band positions compared to their competition. Magenta in Austria has a 20 MHz advantage to A1 in low-band (very good) but trails A1 with 92 MHz in mid-band (not so good). Cosmote slightly follows behind on low-band compared to Vodafone (+10 MHz in their favor), and they head the Greek race with +50 MHz (over Vodafone) in mid-band. All 4 operators should be far ahead of their competitors in network quality. At least if they used their spectrum resources wisely in combination with good (or superior) antenna technologies and a sufficient cellular network footprint. In all else being equal, these 4 operators should be sustainable unbeatable based on their incredible strong spectrum positions. Within Western Europe, I would, over the next few years, expect to see all round best networks with very high best network benchmark scores in Denmark (TDC), Switzerland (Swisscom), Austria (A1), and Greece (Cosmote). Western European countries with relatively more minor surface areas (e.g., <100,000 square km) should outperform much larger countries.

In fact, 3 of the 4 top spectrum-holding operators also have the best cellular networks in their markets. The only exception is A1 in Austria, which lost to Magenta in the most recent Umlaut best network benchmark. Magenta has the best low-band position in the Austrian market, providing for above and beyond cellular indoor-quality coverage that the low-band provides.

There are so many more interesting insights in my collected data. Alas for another article at another time (e.g., topics like the economic value of being the best and winning awards, industry investment levels vs. performance, infrastructure strategies, incumbent vs. later stages operator dynamics, 3.X GHz and mmWave positions in WEU, etc…).

The MNO rank within a country will depend on the relative spectrum position between 1st and 2nd operator. If below 10% (i.e., dark red in chart below), I assess that it will be relative easy for number 2 to match or beat number 1 with improved antenna technology. As the relative strength of the spectrum position of number 1 relative to number 2 is increased, it will become increasingly difficult (assuming number 1 uses an optimal deployment strategy).

The Stars (e.g., #TDCNet / #Nuuday#Swisscom and #EE) have more than a 30% relative spectrum strength compared to the 2nd ranked MNO in a given market. They will have to severely mess up, not to take (or have!) the best cellular network position in their relevant markets. Moreover, network economically, the Stars should have a substantial better Capex position compared to their competitors (although 1 of the Stars seem a “bit” out-of-whack in their sustainable Capex spend, but may be due to fixed broadband focus as well?). As a “cherry on the pie” both Nuuday/TDCNet and Swisscom have some of the strongest spectral overhead positions (i.e., MHz per pop) in Western Europe (relative small populations to very strong spectrum portfolios), which is obviously should enable superior customer experience.

HOW AND HOW NOT TO WIN BEST NETWORK AWARDS.

Out of the 16 cellular operators having the best networks (i.e., rank 1), 12 (75%) also had the strongest (in market) spectrum positions. 3 Operators having the second-best spectrum position ended up taking the best network position, and 1 operator (WindTre, Italy) with the 3rd best spectrum position took the pole network position. The incumbent TIM (Italy) has the strongest spectrum position both in low- (+40 MHz vs. WindTre) and mid-band (+52 MHz vs. WindTre). Clearly, it is not a given that having a superior spectrum position also leads to a superior network position. Though 12 out of 16 operators leverage their spectrum superiority compared to their respective competitors.

For operators with the 2nd largest spectrum position, more variation is observed. 7 out of 16 operators end up with the 2nd position as best network (using Umlaut scoring). 3 ended up as best network, and the rest either in 3rd or 4th position. The reason is that often the difference between 2nd and 3rd spectrum rank position is not per see considerable and therefor, other effects, such as several sites, better antenna technologies, and/or better engineering team, are more likely to be decisive factors.

Nevertheless, the total spectrum is a strong predictor for having the best cellular network and winning the best network award (by Umlaut).

As I have collected quite a rich dataset for mobile operators in Western Europe, it may also be possible to model the expected ranking of operators in a given market. Maybe even reasonably predict an Umlaut score (Hakan, don’t worry, I am not quite there … yet!). This said, while the dataset comprises 58+ operators across 16 markets, more data would be required to increase the confidence in benchmark predictions (if that is what one would like to do). Particular to predict absolute benchmark scores (e.g., voice, data, and crowd) as compiled by Umlaut. Speed benchmarks, ala what Ookla’s provides, are (much) easier to predict with much less sophistication (IMO).

Here I will just show my little toy model using the following rank data (using Jupyter R);

The rank dataset set has 64 rows representing rank data and 5 columns containing (1) performance rank (perf_rank, the response), (2) total spectrum rank (spec_rank, predictor), (3) low-band spectrum rank (lo_spec_rank, predictor), (4) high-band spectrum rank (hi_spec_rank, predictor) and (5) Hz-per-customer rank (hz_cust_rank, predictor).

Concerning the predictor (or feature) Hz-per-customer, I am tracking all cellular operators’ so-called spectrum-overhead, which indicates how much Hz can be assigned to a customer (obviously an over-simplification but nevertheless an indicator). Rank 1 means that there is a significant overhead. That is, we have a lot of spectral capacity per customer. Rank 4 has the opposite meaning: the spectral overhead is small, and we have less spectral capacity per customer. It is good to remember that this particular feature is usually dynamic unless the spectrum situation changes for a given cellular operator (e.g., like traffic and customers may grow).

A (very) simple illustration of the “toy model” is shown below, choosing only low-band and high-band ranks as relevant predictors. Almost 60% of the network-benchmark rank can be explained by the low- and high-band ranks.

The model can, of course, be enriched by including more features, such as effective antenna-capability, Hz-per-Customer, Hz-per-Byte, Coverage KPI, Incident rates, Equipment Aging, Supplier, investment level (over last 2 – 3 years), etc… Given the ongoing debate of the importance of supplier to best networks (and their associated awards), I do not find a particularly strong correlation between RAN (incl. antenna) supplier, network performance, and benchmark rank. The total amount of deployed spectrum is a more important predictor. Of course, given the network performance formula above, if an antenna deployment delivers more effective spectral efficiency (or antenna “boost”) than competitors, it will increase the overall network quality for that operator. However, such an operator would still need to overcompensate the potential lack of spectrum compared to a spectrum-superior competitor.

END THOUGHTS.

Having the best cellular network in a market is something to be very proud of. Winning best network awards is obviously great for an operator and its employees. However, it should really mean that the customers of that best network operator also get the best cellular experience compared to any other operator in that market. A superior customer experience is key.

Firstly, the essential driver (enabler) for best network or network leadership is having a superior spectrum position. In low-band, mid-band, and longer-term also in high-band (e.g., mmWave spectrum). The second is having a good coverage footprint across your market. Compared to competitors, a superior spectrum portfolio could even be with fewer cell sites than a competitor with an inferior spectrum position (forced to densify earlier due to spectral capacity limitations as traffic increases). For a spectrum laggard, building more cell sites is costly (i.e., Capex, Opex, and Time) to attempt to improve or match a superior spectrum competitor. Thirdly, having superior antenna technology deployed is essential. It is also a relatively “easy” way to catch up with a superior competitor, at least in the case of relative minor spectrum position differences. Compared to buying additional spectrum (assuming such is available when you need it) or building out a substantial amount of new cell sites to equalize a cellular performance difference, investing into the best (or better or good-enough-to-win) antenna technology, particular for a spectrum laggard, seems to be the best strategy. Economically, relative to the other two options, and operationally, as time-to-catch-up can be relatively short.

After all, this has been said and done, a superior cellular spectrum portfolio is one of the best predictors for having the best network and even winning the best network award.

Economically, it could imply that a spectrum-superior operator, depending on the spectrum distance to the next-best spectrum position in a given market, may not need to invest in the same level of antenna technology as an inferior operator or could delay such investments to a more opportune moment. This could be important, particularly as advanced antenna development is still at its “toddler” state, and more innovative, powerful (and economical) solutions are expected over the next few years. Though, for operators with relatively minor spectrum differences, the battle will be via the advancement of antenna technology and further cell site sectorization (as opposed to building new sites).

ACKNOWLEDGEMENT.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Also, many of my Deutsche Telekom AG and Industry colleagues, in general, have in countless ways contributed to my thinking and ideas leading to this little Blog. Again, I would like to draw attention to Petr Ledl and his super-competent team in Deutsche Telekom’s Group Research & Trials. Thank you so much for being a constant inspiration and always being available to talk antennas and cellular tech in general.

FURTHER READINGS.

Spectrum Monitoring, “Global Mobile Frequencies Database”, the last update on the database was May 2021. You have a limited amount of free inquiries before you will have to pay an affordable fee for access.

Umlaut, “Umlaut Benchmarking” is an important resources for mobile (and fixed) network benchmarks across the world. The umlaut benchmarking methodology is the de-facto industry standard today and applied in more than 120 countries measuring over 200 mobile networks worldwide. I have also made use of the associated Connect Testlab resouce; www.connect-testlab.com. Most network benchmark data goes back to at least 2017. The Umlaut benchmark is based on in-country drive test for voice and data as well as crowd sourced data. It is by a very big margin The cellular network benchmark to use for ranking cellular operators (imo).

Speedtest (Ookla), “Global Index”, most recent data is Q3, 2021. There are three Western European markets that I have not found any Umlaut (or P3 prior to 2020) benchmarks for; Denmark, France and Norway. For those markets I have (regrettably) had to use Ookla data which is clearly not as rich as Umlaut (at least for public domain data).

5G Standalone – Network Slicing, a Bigger Slice of the Value Pie (Part II)

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Full disclosure … when I was first introduced to the concept of Network Slicing, from one of the 5G fathers that I respect immensely (Rachid, it must have been back at the end of 2014), I thought that it was one of the most useless concepts that I had heard of. I did simply not see (or get) the point of introducing this level of complexity. It did not feel right. My thoughts were that taking the slicing concept to the limit might actually not make any difference to not having it, except for a tremendous amount of orchestration and management overhead (and, of course, besides the technological fun of developing it and getting it to work).

It felt a bit (a lot, actually) as a “let’s do it because we can” thinking. With the “We can” rationale based on the maturity of cloudification and softwarization frameworks, such as cloud-native, public-cloud scale, cloud computing (e.g., edge), software-defined networks (SDN), network-function virtualization (NFV), and the-one-that-is-always-named Artificial Intelligence (AI). I believed there could be other ways to offer the same variety of service experiences without this additional (what I perceived as an unnecessary) complexity. At the time, I had reservations about its impact on network planning, operations, and network efficiency. Not at all sure, it would be a development in the right economic direction.

Since then, I have softened to the concept of Network Slicing. Not (of course) that I have much choice, as slicing is an integral part of 5G standalone (5G) implementation that will be implemented and launched over the next couple of years across our industry. Who knows, I may very likely be proven very wrong, and then I learn something.

What is a network slice? We can see a network slice as an on-user-demand logical separated network partitioning, software-defined on-top of our common physical network infrastructure (wam … what a mouthful … test me out on this one next time you see me), slicing through our network technology stack and its layers. Thinking of a virtual private network (VPN) tunnel through a transport network is a reasonably good analogy. The network slice’s logical partitioning is isolated from other traffic streams (and slices) flowing through the 5G network. Apart from the slice logical isolation, it can have many different customizations, e.g., throughput, latency, scale, Quality of Service, availability, redundancy, security, etc… The user equipment initiates the slice request from a list of pre-defined slice categories. Assuming the network is capable of supporting its requirements, the chosen slice category is then created, orchestrated, and managed through the underlying physical infrastructure that makes up the network stack. The pre-defined slice categories are designed to match what our industry believe is the most essential use-cases, e.g., (a) enhanced mobile broadband use cases (eMBB), (b) ultra-reliable low-latency communications (uRLLC) use cases, (c) massive machine-type communication (MMTC) use cases, (d) Vehicular-to-anything (V2X) use-cases, etc… While the initial (early day) applications of network slicing are expected to be fairly static and configurationally relatively simple, infrastructure suppliers (e.g., Ericsson, Huawei, Nokia, …)expect network slices to become increasingly dynamic and rich in their configuration possibilities. While slicing is typically evoked for B2B and B2B2X, there is not really a reason why consumers could not benefit from network slicing as well (e.g., gaming/VR/AR, consumer smart homes, consumer vehicular applications, etc..).

Show me the money!

Ericsson and Arthur D. Little (ADL) have recently investigated the network slicing opportunities for communications service providers (CSP). Ericsson and ADL have analyzed more than 70 external market reports on the global digitalization of industries and critically reviewed more than 400 5G / digital use cases (see references in Further Readings below). They conclude that the demand from digitalization cannot be served by CSPs without Network Slicing, e.g., “Current network resources cannot match the increasing diversity of demands over time” and “Use cases will not function” (in a conventional mobile network). Thus, according to Ericsson and ADL, the industry can not “live” without Network Slicing (I guess it is good that it comes with 5G SA then). In fact, from their study, they conclude that 30% of the 5G use cases explored would require network slicing (oh joy and good luck that it will be in our networks soon).

Ericsson and ADL find globally a network slicing business potential of 200 Billion US dollars by 2030 for CSPs. With a robust CAGR (i.e., the potential will keep growing) between 23% to 36% by 2030 (i.e., CAGR estimate for period 2025 to 2030). They find that 6 Industries segments take 90+% of the slicing potential(1) Healthcare (23%), (2) Government (17%), (3) Transportation (15%), (4) Energy & Utilities (14%), (5) Manufacturing (12%) and (6) Media & Entertainment (11%). For the keen observer, we see that the verticals are making up for most of the slicing opportunities, with only a relatively small part being related to the consumers. It should, of course, be noted that not all CSPs are necessarily also mobile network operators (MNOs), and there are also outside the strict domain of MNOs revenue potential for non-MNO CSPs (I assume).

Let us compare this slicing opportunity to global mobile industry revenue projections from 2020 to 2030. GSMA has issued a forecast for mobile revenues until 2025, expecting a total turnover of 1,140 Billion US$ in 2025 at a CAGR (2020 – 2025) of 1.26%. Assuming this compounded annual growth rate would continue to apply, we would expect a global mobile industry revenue of 1,213 Bn US$ by 2030. Our 5G deployments will contribute in the order of 621 Bn US$ (or 51% of the total). The incremental total mobile revenue between 2020 and 2030 would be ca. 140 Bn US$ (i.e., 13% over period). If we say that roughly 20% is attributed to mobile B2B business globally, we have that by 2030 we would expect a B2B turnover of 240+ Bn US$ (an increase of ca. 30 Bn US$ over 2020). So, Ericsson & ADL’s 200 Bn US$ network slicing potential is then ca. 16% of the total 2030 global mobile industry turnover or 30+% of the 5G 2030 turnover. Of course, this assumes that somehow the slicing business potential is simply embedded in the existing mobile turnover or attributed to non-MNO CSPs (monetizing the capabilities of the MNO 5G SA slicing enablers).

Of course, the Ericsson-ADL potential could also be an actual new revenue stream untapped by today’s network infrastructures due to the lack of slicing capabilities that 5G SA will bring in the following years. If so, we can look forward to a boost of the total turnover of 16% over the GSMA-based 2030 projection. Given ca. 90% of the slicing potential is related to B2B business, it may imply that B2B mobile business would almost double due to network slicing opportunities (hmmm).

Another recent study assessed that the global 5G network slicing market will reach approximately 18 Bn US$ by 2030 with a CAGR of ca. 41% over 2020-2030.

Irrespective of the slicing turnover quantum, it is unlikely that the new capabilities of 5G SA (including network slicing and much richer granular quality of service framework) will lead to new business opportunities and enable unexplored use cases. That, in turn, may indeed lead to enhanced monetization opportunities and new revenue streams between now (2022) and 2030 for our industry.

Most Western European markets will see 5G SA being launched over the next 2 to 3 years; as 5G penetration rapidly approaches 50% penetration, I expect network slicing use cases being to be tried out with CSP/MNOs, industry partners, and governmental institutions soon after 5G SA has been launched. It should be pointed out that already for some years, slicing concepts have been trialed out in various settings. Both in 4G as well as 5G NSA networks.

Prologue to Network Slicing.

5G comes with a lot of fundamental capabilities as shown in the picture below,

5G allows for (1) enhanced mobile broadband, (2) very low latency, (3) massive increase in device density handling, i.e., massive device scale-up, (4) ultra-higher network reliability and service availability, and (5) enhanced security (not shown in the above diagram) compared to previous Gs.

The service (and thus network) requirement combinations are very high. The illustration below shows two examples of mapped-out sub-set of service (and therefore also eventually slice) requirements mapped onto the major 5G capabilities. In addition, it is quite likely that businesses would have additional requirements related to slicing performance monitoring, for example, in real-time across the network stack.

and with all the various industrial or vertical use cases (see below) one could imagine (noting that there may be many many more outside our imagination), the “fathers” of 5G became (very) concerned with how such business-critical services could be orchestrated and managed within a traditional mobile network architecture as well as across various public land mobile networks (PLMN). Much of this also comes out of the wish that 5G should “conquer” (take a slice of) next-generation industries (i.e., Industry 4.0), providing additional value above and beyond “the dumb bit pipe.” Moreover, I do believe that in parallel with the wish of becoming much more relevant to Industry 4.0 (and the next generation of verticals requirements), what also played a role in the conception of network slicing is the deeply rooted engineering concept of “control being better than trust” and that “centralized control is better than decentralized” (I lost count on this debate of centralized control vs. distributed management a long time ago).

So, yes … The 5G world is about to get a lot more complex in terms of Industrial use cases that 5G should support. And yes, our consumers will expect much higher download speeds, real-time (whatever that will mean) gaming capabilities, and “autonomous” driving …

“… it’s clear that the one shared public network cannot meet the needs of emerging and advanced mobile connectivity use cases, which have a diverse array of technical operations and security requirements.” (quote from Ericsson and Arthur D. Little study, 2021).

“The diversity of requirements will only grow more disparate between use cases — the one-size-fits-all approach to wireless connectivity will no longer suffice.” (quote from Ericsson and Arthur D. Little study, 2021).

Being a naturalist (yes, I like “naked” networks), it does seem somewhat odd (to me) to say that next generation (e.g., 5G) networks cannot support all the industrious use cases that we may throw at it in its native form. Particular after having invested billions in such networks. By partitioning a network up in limiting (logically isolated), slice instances can all be supported (allegedly). I am still in the thinking phase on that one (but I don’t think the math adds up).

Now, whether one agrees (entirely) with the economic sentiment expressed by Ericsson and ADL or not. We need a richer granular way of orchestrating and managing all those diverse use-cases we expect our 5G network to support.

Network Slicing.

So, we have (or will get) network slicing with our 5G SA Core deployment. As a reminder, when we talk about a network slice, we mean;

“An on-user-demand logical separated network partitioning, software-defined, on-top of a common physical network infrastructure.”

So, the customer requested the network slice, typically via a predefined menu of slicing categories that may also have been pre-validated by the relevant network. Requested slices can also be Customized, by the requester, within the underlying 5G infrastructure capabilities and functionalities. If the network can provide the requested slicing requirements, the slice is (in theory) granted. The core network then orchestrates a logically separated network partitioning throughout the relevant infrastructure resources to comply with the requested requirements (e.g., speed, latency, device scale, coverage, security, etc…). The requested partitioning (i.e., the slice) is isolated from other slices to enable (at least on a logical level) independence of other live slices. Slice Isolation is an essential concept to network slicing. Slice Elasticity ensures that resources can be scaled up and down to ensure individual slice efficiency and an overall efficient operation of all operating slices. It is possible to have a single individual network slice or partition a slice into sub-slices with their individual requirements (that does not breach the overarching slice requirements). GSMA has issued roaming and inter-PLMN guidelines to ensure 5G network slicing inter-operability when a customer’s application finds itself outside its home -PLMN.

Today, and thanks to GSMA and ITU, there are some standard network slice services pre-defined, such as (a) eMBB – Enhanced Mobile Broadband, (b) mMTC – Massive machine-type communications, (c) URLLC – Ultra-reliable low-latency communications, (d) V2X – Vehicular-to-anything communications. These identified standard network slices are called Slice Service Types (SST). SSTs are not only limited to above mentioned 4 pre-defined slice service types. The SSTs are matched to what is called a Generic Slice Template (GST) that currently, we have 37 slicing attributes, allowing for quite a big span of combinations of requirements to be specified and validated against network capabilities and functionalities (maybe there is room for some AI/ML guidance here).

The user-requested network slice that has been set up end-2-end across the network stack, between the 5G Core and the user equipment, is called the network slice instance. The whole slice setup procedure is very well described in Chapter 12 of “5G NR and enhancements, from R15 to R16. The below illustration provides a high-level illustration of various network slices,

The 5G control function Access and Mobility management Function (AMF) is the focal point for the network slicing instances. This particular architectural choice does allow for other slicing control possibilities with a higher or lower degree of core network functionality sharing between slice instances. Again the technical details are explained well in some of the reading resources provided below. The takeaway from the above illustration is that the slice instance specifications are defined for each layer and respective physical infrastructure (e.g., routers, switches, gateways, transport device in general, etc…) of the network stack (e.g., Telco Core Cloud, Backbone, Edge Cloud, Fronthaul, New Radio, and its respective air-interface). Each telco stack layer that is part of a given network slice instance is supposed to adhere strictly to the slice requirements, enabling an End-2-End, from Core to New Radio through to the user equipment, slice of a given quality (e.g., speed, latency, jitter, security, availability, etc..).

And it may be good to keep in mind that although complex industrial use cases get a lot of attention, voice and mobile broadband could easily be set up with their own slice instances and respective quality-of-services.

Network slicing examples.

All the technical network slicing “stuff” is pretty much-taken care of by standardization and provided by the 5G infrastructure solution providers (e.g., Mavenir, Huawei, Ericsson, Nokia, etc..). Figuring the technical details of how these works require an engineering or technical background and a lot of reading.

As I see it, the challenge will be in figuring out, given a use-case, the slicing requirements and whether a single slice instance suffice or multiple are required to provide the appropriate operations and fulfillment. This, I expect, will be a challenge for both the mobile network operator as well as the business partner with the use case. This assumes that the economics will come out right for more complex (e.g., dynamic) and granular slice-instance use cases. For the operator as well as for businesses and public institutions.

The illustration below provides examples of a few (out of the 37) slicing attributes for different use cases, (a) Factories with time-critical, non-time-critical, and connected goods sub-use cases (e.g., sub-slice instances, QoS differentiated), (b) Automotive with autonomous, assisted and shared view sub-use cases, (c) Health use cases, and (d) Energy use cases.

One case that I have been studying is Networked Robotics use cases for the industrial segment. Think here about ad-hoc robotic swarms (for agricultural or security use cases) or industrial production or logistics sorting lines; below are some reflections around that.

End thoughts.

With the emergence of the 5G Core, we will also get the possibility to apply Network slicing to many diverse use cases. That there are interesting business opportunities with network slicing, I think, is clear. Whether it will add 16% to the global mobile topline by 2030, I don’t know and maybe also somewhat skeptical about (but hey, if it does … fantastic).

Today, the type of business opportunities that network slicing brings in the vertical segments is not a very big part of a mobile operator’s core competence. Mobile operators with 5G network slicing capabilities ultimately will need to build up such competence or (and!) team up with companies that have it.

That is, if the future use cases of network slicing, as envisioned by many suppliers, ultimately will get off the ground economically as well as operationally. I remain concerned that network slicing will not make operators’ operations less complex and thus will add cost (and possible failures) to their balance sheets. The “funny” thing (IMO) is that when our 5G networks are relatively unloaded, we would not have a problem delivering the use cases (obviously). Once our 5G networks are loaded, network slicing may not be the right remedy to manage traffic pressure situations or would make the quality we are providing to consumers progressively worse (and I am not sure that business and value-wise, this is a great thing to do). Of course, 6G may solve all those concerns 😉

Acknowledgement.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Also, many of my Deutsche Telekom AG and Industry colleagues, in general, have in countless ways contributed to my thinking and ideas leading to this little Blog. Thank you!

Further readings.

Kim Kyllesbech Larsen, “5G Standalone – European Demand & Expectations (Part I).”, LinkedIn article, (December 2021).

Kim Kyllesbech Larsen, “5G Economics – The Numbers (Appendix X).”, Techneconomyblog.com, (July 2017).

Kim Kyllesbech Larsen, “5G Economics – The Tactile Internet (Chapter 2)”, Techneconomyblog.com, (January 2017).

Henrik Bailier, Jan Lemark, Angelo Centonza, and Thomas Aasberg, “Applied network slicing scenarios in 5G”, Ericsson Technology Review, (February 2021).

Ericsson and Arthur D. Little, “Network slicing: A go-to-market guide to capture the high revenue potential”, Ericsson.com, (2021). The study concludes that network slicing is a 200 Bn. US$ opportunity for CSPs by 2030. It is 1 out of 4 reports on network slicing. See also “Network slicing: Top 10 use cases to target”, “The essential building blocks of E2E network slicing” and “The network slicing transformation journey“.

 S. O’Dea, “Global mobile industry revenue from 2016 to 2025″, (March, 2021).

S. M. Ahsan Kazmi, Latif U.Khan, Nguyen H. Tran, and Choong Seon Hong, “Network Slicing for 5G and Beyond Networks”, Springer International Publishing, (2019). 

Jia Shen, Zhongda Du, & Zhi Zhang, “5G NR and enhancements, from R15 to R16”, Elsevier Science, (2021). Provides a really good overview of what to expect from 5G standalone. Chapter 12 provides a good explanation of (and in detail account for) how 5G Network Slicing works in detail. Definitely one of my favorite books on 5G, it is not “just” an ANRA.

GSMA Association, “An Introduction to Network Slicing”, (2017). A very good introduction to Network slicing.

ITU-T, “Network slice orchestration and management for providing network services to 3rd party in the IMT-2020 network”, Recommendation ITU-T Y.3153 (2019). Describing high-level customer slice request for instantiation, changes and ultimately the termination.

Claudia Campolo, Antonella Molinaro, Antonio Lera, and Francesco Menichella, “5G Network Slicing for Vehicle-to-Everything Services”, IEEE Wireless Communications 24, (December 2017). Great account of how network slicing should work for V2X services.

GSMA, “Securing the 5G Era” (2021). A good overview of security principles in 5G and how previous vulnerabilities in previous cellular generations are being addressed in 5G. This includes some explanation on why slicing further enhances security.