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 increasecustomer 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.
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.
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog.
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.
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.
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.
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.
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;
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.
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.
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;
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.
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.
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.
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;
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.
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.
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↑).
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;
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.
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.
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).
Modernization activities are not covered by cloudification investments or business requirements.
Development support for Finance (e.g., ERP/SAP requirements), HR requirements, and other miscellaneous activities not captured above.
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.
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.
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.
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.
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.
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.
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).
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”.
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.
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.
New Street Research, “European 5G deep-dive”, (July, 2021).
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).
100% 5G coverage is not going to happen with 30 – 300 GHz millimeter-wave frequencies alone.
The “NGMN 5G white paper” , which I will in the subsequent parts refer to as the 5G vision paper, require the 5G coverage to be 100%.
At 100% cellular coverage it becomes somewhat academic whether we talk about population coverage or geographical (area) coverage. The best way to make sure you cover 100% of population is covering 100% of the geography. Of course if you cover 100% of the geography, you are “reasonably” ensured to cover 100% of the population.
While it is theoretically possible to cover 100% (or very near to) of population without covering 100% of the geography, it might be instructive to think why 100% geographical coverage could be a useful target in 5G;
Network-augmented driving and support for varous degrees of autonomous driving would require all roads to be covered (however small).
Internet of Things (IoT) Sensors and Actuators are likely going to be of use also in rural areas (e.g., agriculture, forestation, security, waterways, railways, traffic lights, speed-detectors, villages..) and would require a network to connect to.
Given many users personal area IoT networks (e.g., fitness & health monitors, location detection, smart-devices in general) ubiquitous becomes essential.
Internet of flying things (e.g., drones) are also likely to benefit from 100% area and aerial coverage.
However, many countries remain lacking in comprehensive geographical coverage. Here is an overview of the situation in EU28 (as of 2015);
For EU28 countries, 14% of all house holds in 2015 still had no LTE coverage. This was approx.30+ million households or equivalent to 70+ million citizens without LTE coverage. The 14% might seem benign. However, it covers a Rural neglect of 64% of households not having LTE coverage. One of the core reasons for the lack of rural (population and household) coverage is mainly an economic one. Due to the relative low number of population covered per rural site and compounded by affordability issues for the rural population, overall rural sites tend to have low or no profitability. Network sharing can however improve the rural site profitability as site-related costs are shared.
From an area coverage perspective, the 64% of rural households in EU28 not having LTE coverage is likely to amount to a sizable lack of LTE coverage area. This rural proportion of areas and households are also very likely by far the least profitable to cover for any operator possibly even with very progressive network sharing arrangements.
Fixed broadband, Fiber to the Premises (FTTP) and DOCSIS3.0, lacks further behind that of mobile LTE-based broadband. Maybe not surprisingly from an business economic perspective, in rural areas fixed broadband is largely unavailable across EU28.
The chart below illustrates the variation in lack of broadband coverage across LTE, Fiber to the Premises (FTTP) and DOCSIS3.0 (i.e., Cable) from a total country perspective (i.e., rural areas included in average).
We observe that most countries have very far to go on fixed broadband provisioning (i.e., FTTP and DOCSIS3.0) and even on LTE coverage lacks complete coverage. The rural coverage view (not shown here) would be substantially worse than the above Total view.
The 5G ambition is to cover 100% of all population and households. Due to the demographics of how rural households (and populations) are spread, it is also likely that fairly large geographical areas would need to be covered in order to come true on the 100% ambition.
It would appear that bridging this lack of broadband coverage would be best served by a cellular-based technology. Given the fairly low population density in such areas relative higher average service quality (i.e., broadband) could be delivered as long as the cell range is optimized and sufficient spectrum at a relative low carrier frequency (< 1 GHz) would be available. It should be remembered that the super-high 5G 1 – 10 Gbps performance cannot be expected in rural areas. Due to the lower carrier frequency range need to provide economic rural coverage both advanced antenna systems and very large bandwidth (e.g., such as found in the mm-frequency range) would not be available to those areas. Thus limiting the capacity and peak performance possible even with 5G.
I would suspect that irrespective of the 100% ambition, telecom providers would be challenged by the economics of cellular deployment and traffic distribution. Rural areas really sucks in profitability, even in fairly aggressive sharing scenarios. Although multi-party (more than 2) sharing might be a way to minimize the profitability burden on deep rural coverage.
The above chart shows the relationship between traffic distribution and sites. As a rule of thumb 50% of revenue is typically generated by 10% of all sites (i.e., in a normal legacy mobile network) and approx. 50% of (rural) sites share roughly 10% of the revenue. Note: in emerging markets the distribution is somewhat steeper as less comprehensive rural coverage typically exist. (Source:The ABC of Network Sharing – The Fundamentals.).
Irrespective of my relative pessimism of the wider coverage utility and economics of millimeter-wave (mm-wave) based coverage, there shall be no doubt that mm-wave coverage will be essential for smaller and smallest cell coverage where due to density of users or applications will require extreme (in comparison to today’s demand) data speeds and capacities. Millimeter-wave coverage-based architectures offer very attractive / advanced antenna solutions that further will allow for increased spectral efficiency and throughput. Also the possibility of using mm-wave point to multipoint connectivity as last mile replacement for fiber appears very attractive in rural and sub-urban clutters (and possible beyond if the cost of the electronics drop according the expeced huge increase in demand for such). This last point however is in my opinion independent of 5G as Facebook with their Terragraph development have shown (i.e., 60 GHz WiGig-based system). A great account for mm-wave wireless communications systems can be found in T.S. Rappaport et al.’s book “Millimeter Wave Wireless Communications” which not only comprises the benefits of mm-wave systems but also provides an account for the challenges. It should be noted that this topic is still a very active (and interesting) research area that is relative far away from having reached maturity.
In order to provide 100% 5G coverage for the mass market of people & things, we need to engage the traditional cellular frequency bands from 600 MHz to 3 GHz.
1 – 10 Gbps PEAK DATA RATE PER USER.
Getting a Giga bit per second speed is going to require a lot of frequency bandwidth, highly advanced antenna systems and lots of additional cells. And that is likely going to lead to a (very) costly 5G deployment. Irrespective of the anticipated reduced unit cost or relative cost per Byte or bit-per-second.
At 1 Gbps it would take approx. 16 seconds to download a 2 GB SD movie. It would take less than a minute for the HD version (i.e., at 10 Gbps it just gets better;-). Say you have a 16GB smartphone, you loose maybe up to 20+% for the OS, leaving around 13GB for things to download. With 1Gbps it would take less than 2 minutes to fill up your smartphones storage (assuming you haven’t run out of credit on your data plan or reached your data ceiling before then … of course unless you happen to be a customer of T-Mobile US in which case you can binge on = you have no problems!).
The biggest share of broadband usage comes from video streaming which takes up 60% to 80% of all volumetric traffic pending country (i.e., LTE terminal penetration dependent). Providing higher speed to your customer than is required by the applied video streaming technology and smartphone or tablet display being used, seems somewhat futile to aim for. The Table below provides an overview of streaming standards, their optimal speeds and typical viewing distance for optimal experience;
So … 1Gbps could be cool … if we deliver 32K video to our customers end device, i.e., 750 – 1600 Mbps optimal data rate. Though it is hard to see customers benefiting from this performance boost given current smartphone or tablet display sizes. The screen size really have to be ridiculously large to truly benefit from this kind of resolution. Of course Star Trek-like full emersion (i.e., holodeck) scenarios would arguably require a lot (=understatement) bandwidth and even more (=beyond understatement) computing power … though such would scenario appears unlikely to be coming out of cellular devices (even in Star Trek).
1 Gbps fixed broadband plans have started to sell across Europe. Typically on Fiber networks although also on DOCSIS3.1 (10Gbps DS/1 Gbps US) networks as well in a few places. It will only be a matter of time before we see 10 Gbps fixed broadband plans being offered to consumers. Irrespective of compelling use cases might be lacking it might at least give you the bragging rights of having the biggest.
From European Commissions “Europe’s Digital Progress Report 2016”, 22 % of European homes subscribe to fast broadband access of at least 30 Mbps. An estimated 8% of European households subscribe to broadband plans of at least 100 Mbps. It is worth noticing that this is not a problem with coverage as according with the EC’s “Digital Progress Report” around 70% of all homes are covered with at least 30 Mbps and ca. 50% are covered with speeds exceeding 100 Mbps.
The chart below illustrates the broadband speed coverage in EU28;
Even if 1Gbps fixed broadband plans are being offered, still majority of European homes are at speeds below the 100 Mbps. Possible suggesting that affordability and household economics plays a role as well as the basic perceived need for speed might not (yet?) be much beyond 30 Mbps?
Most aggregation and core transport networks are designed, planned, built and operated on a assumption of dominantly customer demand of lower than 100 Mbps packages. As 1Gbps and 10 Gbps gets commercial traction, substantial upgrades are require in aggregation, core transport and last but not least possible also on an access level (to design shorter paths). It is highly likely distances between access, aggregation and core transport elements are too long to support these much higher data rates leading to very substantial redesigns and physical work to support this push to substantial higher throughputs.
Most telecommunications companies will require very substantial investments in their existing transport networks all the way from access to aggregation through the optical core switching networks, out into the world wide web of internet to support 1Gbps to 10 Gbps. Optical switching cards needs to be substantially upgraded, legacy IP/MPLS architectures might no longer work very well (i.e., scale & complexity issue).
Most analysts today believe that incumbent fixed & mobile broadband telecommunications companies with a reasonable modernized transport network are best positioned for 5G compared to mobile-only operators or fixed-mobile incumbents with an aging transport infrastructure.
What about the state of LTE speeds across Europe? OpenSignal recurrently reports on the State of LTE, the following summarizes LTE speeds in Mbps as of June 2017 for EU28 (with the exception of a few countries not included in the OpenSignal dataset);
The OpenSignal measurements are based on more than half a million devices, almost 20 billion measurements over the period of the 3 first month of 2017.
The 5G speed ambition is by todays standards 10 to 30+ times away from present 2016/2017 household fixed broadband demand or the reality of provided LTE speeds.
In essence, I can provide very high data rates in bits per second by providing a lot of frequency bandwidth B, use the most spectrally efficient technologies maximizing η, and/or add as many cells N that my economics allow for.
The average spectral efficiency is expected to be coming out in the order of 10 Mbps/MHz/cell using advanced receiver architectures, multi-antenna, multi-cell transmission and corporation. So pretty much all the high tech goodies we have in the tool box is being put to use of squeezing out as many bits per spectral Hz available and in a sustainable matter. Under very ideal Signal to Noise Ratio conditions, massive antenna arrays of up to 64 antenna elements (i.e., an optimum) seems to indicate that 50+ Mbps/MHz/Cell might be feasible in peak.
So for a spectral efficiency of 10 Mbps/MHz/cell and a demanded 1 Gbps data rate we would need 100 MHz frequency bandwidth per cell (i.e., using the above formula). Under very ideal conditions and relative large antenna arrays this might lead to a spectral requirement of only 20 MHz at 50 Mbps/MHz/Cell. Obviously, for 10 Gbps data rate we would require 1,000 MHz frequency bandwidth (1 GHz!) per cell at an average spectral efficiency of 10 Mbps/MHz/cell.
The spectral efficiency assumed for 5G heavily depends on successful deployment of many-antenna segment arrays (e.g., Massive MiMo, beam-forming antennas, …). Such fairly complex antenna deployment scenarios work best at higher frequencies, typically above 2GHz. Also such antenna systems works better at TDD than FDD with some margin on spectral efficiency. These advanced antenna solutions works perfectly in the millimeter wave range (i.e., ca. 30 – 300 GHz) where the antenna segments are much smaller and antennas can be made fairly (very) compact (note: resonance frequency of the antenna proportional to half the wavelength with is inverse proportional to the carrier frequency and thus higher frequencies need smaller material dimension to operate).
Below 2 GHz higher-order MiMo becomes increasingly impractical and the spectral efficiency regress to the limitation of a simple single-path antenna. Substantially lower than what can be achieved at much high frequencies with for example massive-MiMo.
So for the 1Gbps to 10 Gbps data rates to work out we have the following relative simple rationale;
High data rates require a lot of frequency bandwidth (>100 MHz to several GHz per channel).
Lots of frequency bandwidth are increasingly easier to find at high and very high carrier frequencies (i.e., why millimeter wave frequency band between 30 – 300 GHz is so appealing).
High and very high carrier frequencies results in small, smaller and smallest cells with very high bits per second per unit area (i.e., the area is very small!).
High and very high carrier frequency allows me to get the most out of higher order MiMo antennas (i.e., with lots of antenna elements),
Due to fairly limited cell range, I boost my overall capacity by adding many smallest cells (i.e., at the highest frequencies).
We need to watch out for the small cell densification which tends not to scale very well economically. The scaling becomes a particular problem when we need hundreds of thousands of such small cells as it is expected in most 5G deployment scenarios (i.e., particular driven by the x1000 traffic increase). The advanced antenna systems required (including the computation resources needed) to max out on spectral efficiency are likely going to be one of the major causes of breaking the economical scaling. Although there are many other CapEx and OpEx scaling factors to be concerned about for small cell deployment at scale.
Further, for mass market 5G coverage, as opposed to hot traffic zones or indoor solutions, lower carrier frequencies are needed. These will tend to be in the usual cellular range we know from our legacy cellular communications systems today (e.g., 600 MHz – 2.1 GHz). It should not be expected that 5G spectral efficiency will gain much above what is already possible with LTE and LTE-advanced at this legacy cellular frequency range. Sheer bandwidth accumulation (multi-frequency carrier aggregation) and increased site density is for the lower frequency range a more likely 5G path. Of course mass market 5G customers will benefit from faster reaction times (i.e., lower latencies), higher availability, more advanced & higher performing services arising from the very substantial changes expected in transport networks and data centers with the introduction of 5G.
Last but not least to this story … 80% and above of all mobile broadband customers usage, data as well as voice, happens in very few cells (e.g., 3!) … representing their Home and Work.
As most of the mobile cellular traffic happen at the home and at work (i.e., thus in most cases indoor) there are many ways to support such traffic without being concerned about the limitation of cell ranges.
The giga bit per second cellular service is NOT a service for the mass market, at least not in its macro-cellular form.
≤ 1 ms IN ROUND-TRIP DELAY.
A total round-trip delay of 1 or less millisecond is very much attuned to niche service. But a niche service that nevertheless could be very costly for all to implement.
Speed of light travels ca. 300 km per millisecond (ms) in vacuum and approx. 210 km per ms in fiber (some material dependency here). Lately engineers have gotten really excited about the speed of light not being fast enough and have made a lot of heavy thinking abou edge this and that (e.g., computing, cloud, cloudlets, CDNs,, etc…). This said it is certainly true that most modern data centers have not been build taking too much into account that speed of light might become insufficient. And should there really be a great business case of sub-millisecond total (i.e., including the application layer) roundtrip time scales edge computing resources would be required a lot closer to customers than what is the case today.
It is common to use delay, round-trip time or round-trip delay, or latency as meaning the same thing. Though it is always cool to make sure people really talk about the same thing by confirming that it is indeed a round-trip rather than single path. Also to be clear it is worthwhile to check that all people around the table talk about delay at the same place in the OSI stack or network path or whatever reference point agreed to be used.
In the context of the 5G vision paper it is emphasized that specified round-trip time is based on the application layer (i.e., OSI model) as reference point. It is certainly the most meaningful measure of user experience. This is defined as the End-2-End (E2E) Latency metric and measure the complete delay traversing the OSI stack from physical layer all the way up through network layer to the top application layer, down again, between source and destination including acknowledgement of a successful data packet delivery.
The 5G system shall provide 10 ms E2E latency in general and 1 ms E2E latency for use cases requiring extremely low latency.
The 5G vision paper states “Note these latency targets assume the application layer processing time is negligible to the delay introduced by transport and switching.” (Section 4.1.3 page 26 in “NGMN 5G White paper”).
In my opinion it is a very substantial mouthful to assume that the Application Layer (actually what is above the Network Layer) will not contribute significantly to the overall latency. Certainly for many applications residing outside the operators network borders, in the world wide web, we can expect a very substantial delay (i.e., even in comparison with 10 ms). Again this aspect was also addressed in my two first chapters.
Very substantial investments are likely needed to meet E2E delays envisioned in 5G. In fact the cost of improving latencies gets prohibitively more expensive as the target is lowered. The overall cost of design for 10 ms would be a lot less costly than designing for 1 ms or lower. The network design challenge if 1 millisecond or below is required, is that it might not matter that this is only a “service” needed in very special situations, overall the network would have to be designed for the strictest denominator.
Moreover, if remedies needs to be found to mitigate likely delays above the Network Layer, distance and insufficient speed of light might be the least of worries to get this ambition nailed (even at the 10 ms target). Of course if all applications are moved inside operator’s networked premises with simpler transport paths (and yes shorter effective distances) and distributed across a hierarchical cloud (edge, frontend, backend, etc..), the assumption of negligible delay in layers above the Network Layer might become much more likely. However, it does sound a lot like America Online walled garden fast forward to the past kind of paradigm.
So with 1 ms E2E delay … yeah yeah … “play it again Sam” … relevant applications clearly need to be inside network boundary and being optimized for processing speed or silly & simple (i.e., negligible delay above the Network Layer), no queuing delay (to the extend of being in-efficiency?), near-instantaneous transmission (i.e., negligible transmission delay) and distances likely below tenth of km (i.e., very short propagation delay).
When the speed of light is too slow there are few economic options to solve that challenge.
≥ 10,000 Gbps / Km2 DATA DENSITY.
The data density is maybe not the most sensible measure around. If taken too serious could lead to hyper-ultra dense smallest network deployments.
This has always been a fun one in my opinion. It can be a meaningful design metric or completely meaningless.
There is of course nothing particular challenging in getting a very high throughput density if an area is small enough. If I have a cellular range of few tens of meters, say 20 meters, then my cell area is smaller than 1/1000 of a km2. If I have 620 MHz bandwidth aggregated between 28 GHz and 39 GHz (i.e., both in the millimeter wave band) with a 10 Mbps/MHz/Cell, I could support 6,200 Gbps/km2. That’s almost 3 Petabyte in an hour or 10 years of 24/7 binge watching of HD videos. Note given my spectral efficiency is based on an average value, it is likely that I could achieve substantially more bandwidth density and in peaks closer to the 10,000 Gbps/km2 … easily.
Pretty Awesome Wow!
The basic; a Terabit equals 1024 Gigabits (but I tend to ignore that last 24 … sorry I am not).
With a traffic density of ca. 10,000 Gbps per km2, one would expect to have between 1,000 (@ 10 Gbps peak) to 10,000 (@ 1 Gbps peak) concurrent users per square km.
At 10 Mbps/MHz/Cell one would expect to have a 1,000 Cell-GHz/km2. Assume that we would have 1 GHz bandwidth (i.e., somewhere in the 30 – 300 GHz mm-wave range), one would need 1,000 cells per km2. On average with a cell range of about 20 meters (smaller to smallest … I guess what Nokia would call an Hyper-Ultra-Dense Network;-). Thus each cell would minimum have between 1 to 10 concurrent users.
Just as a reminder! 1 minutes at 1 Gbps corresponds to 7.5 GB. A bit more than what you need for a 80 minute HD (i.e., 720pp) full movie stream … in 1 minutes. So with your (almost) personal smallest cell what about the remaining 59 minutes? Seems somewhat wasteful at least until kingdom come (alas maybe sooner than that).
It would appear that the very high 5G data density target could result in very in-efficient networks from a utilization perspective.
≥ 1 MN / Km2 DEVICE DENSITY.
One million 5G devices per square kilometer appears to be far far out in a future where one would expect us to be talking about 7G or even higher Gs.
1 Million devices seems like a lot and certainly per km2. It is 1 device per square meter on average. A 20 meter cell-range smallest cell would contain ca. 1,200 devices.
To give this number perspective lets compare it with one of my favorite South-East Asian cities. The city with one of the highest population densities around, Manila (Philippines). Manila has more than 40 thousand people per square km. Thus in Manila this would mean that we would have about 24 devices per person or 100+ per household per km2. Overall, in Manila we would then expect approx. 40 million devices spread across the city (i.e., Manila has ca. 1.8 Million inhabitants over an area of 43 km2. Philippines has a population of approx. 100 Million).
Just for the curious, it is possible to find other more populated areas in the world. However, these highly dense areas tends to be over relative smaller surface areas, often much smaller than a square kilometer and with relative few people. For example Fadiouth Island in Dakar have a surface area of 0.15 km2 and 9,000 inhabitants making it one of the most pop densest areas in the world (i.e., 60,000 pop per km2).
I hope I made my case! A million devices per km2 is a big number.
Let us look at it from a forecasting perspective. Just to see whether we are possibly getting close to this 5G ambition number.
IHS forecasts 30.5 Billion installed devices by 2020, IDC is also believes it to be around 30 Billion by 2020. Machina Research is less bullish and projects 27 Billion by 2025 (IHS expects that number to be 75.4 Billion) but this forecast is from 2013. Irrespective, we are obviously in the league of very big numbers. By the way 5G IoT if at all considered is only a tiny fraction of the overall projected IoT numbers (e.g., Machine Research expects 10 Million 5G IoT connections by 2024 …that is extremely small numbers in comparison to the overall IoT projections).
To break this number down to something that could be more meaningful than just being Big and impressive, let just establish a couple of worldish numbers that can help us with this;
2020 population expected to be around 7.8 Billion compared to 2016 7.4 Billion.
Global pop per HH is ~3.5 (average number!) which might be marginally lower in 2020. Urban populations tend to have less pop per households ca. 3.0. Urban populations in so-called developed countries are having a pop per HH of ca. 2.4.
ca. 55% of world population lives in Urban areas. This will be higher by 2020.
Less than 20% of world population lives in developed countries (based on HDI). This is a 2016 estimate and will be higher by 2020.
World surface area is 510 Million km2 (including water).
of which ca. 150 million km2 is land area
of which ca. 75 million km2 is habitable.
of which 3% is an upper limit estimate of earth surface area covered by urban development, i.e., 15.3 Million km2.
of which approx. 1.7 Million km2 comprises developed regions urban areas.
ca. 37% of all land-based area is agricultural land.
Using 30 Billion IoT devices by 2020 is equivalent to;
ca. 4 IoT per world population.
ca. 14 IoT per world households.
ca. 200 IoT per km2 of all land-based surface area.
ca. 2,000 IoT per km2 of all urban developed surface area.
If we limit IoT’s in 2020 to developed countries, which wrongly or rightly exclude China, India and larger parts of Latin America, we get the following by 2020;
ca. 20 IoT per developed country population.
ca. 50 IoT per developed country households.
ca. 18,000 IoT per km2 developed country urbanized areas.
Given that it would make sense to include larger areas and population of both China, India and Latin America, the above developed country numbers are bound to be (a lot) lower per Pop, HH and km2. If we include agricultural land the number of IoTs will go down per km2.
So far far away from a Million IoT per km2.
What about parking spaces, for sure IoT will add up when we consider parking spaces!? … Right? Well in Europe you will find that most big cities will have between 50 to 200 (public) parking spaces per square kilometer (e.g., ca. 67 per km2 for Berlin and 160 per km2 in Greater Copenhagen). Aha not really making up to the Million IoT per km2 … what about cars?
In EU28 there are approx. 256 Million passenger cars (2015 data) over a population of ca. 510 Million pops (or ca. 213 million households). So a bit more than 1 passenger car per household on EU28 average. In Eu28 approx. 75+% lives in urban area which comprises ca. 150 thousand square kilometers (i.e., 3.8% of EU28’s 4 Million km2). So one would expect little more (if not a little less) than 1,300 passenger cars per km2. You may say … aha but it is not fair … you don’t include motor vehicles that are used for work … well that is an exercise for you (too convince yourself why that doesn’t really matter too much and with my royal rounding up numbers maybe is already accounted for). Also consider that many EU28 major cities with good public transportation are having significantly less cars per household or population than the average would allude to.
Surely, public street light will make it through? Nope! Typical bigger modern developed country city will have on average approx. 85 street lights per km2, although it varies from 0 to 1,000+. Light bulbs per residential household (from a 2012 study of the US) ranges from 50 to 80+. In developed countries we have roughly 1,000 households per km2 and thus we would expect between 50 thousand to 80+ thousand lightbulbs per km2. Shops and business would add some additions to this number.
With a cumulated annual growth rate of ca. 22% it would take 20 years (from 2020) to reach a Million IoT devices per km2 if we will have 20 thousand per km2 by 2020. With a 30% CAGR it would still take 15 years (from 2020) to reach a Million IoT per km2.
The current IoT projections of 30 Billion IoT devices in operation by 2020 does not appear to be unrealistic when broken down on a household or population level in developed areas (even less ambitious on a worldwide level). The 18,000 IoT per km2 of developed urban surface area by 2020 does appear somewhat ambitious. However, if we would include agricultural land the number would become possible a more reasonable.
If you include street crossings, traffic radars, city-based video monitoring (e.g., London has approx. 300 per km2, Hong Kong ca. 200 per km2), city-based traffic sensors, environmental sensors, etc.. you are going to get to sizable numbers.
Maybe the 1 Million Devices per km2 ambition is not one of the most important 5G design criteria’s for the short term (i.e., next 10 – 20 years).
Oh and most IoT forecasts from the period 2015 – 2016 does not really include 5G IoT devices in particular. The chart below illustrates Machina Research IoT forecast for 2024 (from August 2015). In a more recent forecast from 2016, Machine Research predict that by 2024 there would be ca. 10 million 5G IoT connections or 0.04% of the total number of forecasted connections;
The winner is … IoTs using WiFi or other short range communications protocols. Obviously, the cynic in me (mea culpa) would say that a mm-wave based 5G connections can also be characterized as short range … so there might be a very interesting replacement market there for 5G IoT … maybe? 😉
Expectations to 5G-based IoT does not appear to be very impressive at least over the next 10 years and possible beyond.
The un-importance of 5G IoT should not be a great surprise given most 5G deployment scenarios are focused on millimeter-wave smallest 5G cell coverage which is not good for comprehensive coverage of IoT devices not being limited to those very special 5G coverage situations being thought about today.
Only operators focusing on comprehensive 5G coverage re-purposing lower carrier frequency bands (i.e., 1 GHz and lower) can possible expect to gain a reasonable (as opposed to niche) 5G IoT business. T-Mobile US with their 600 MHz 5G strategy might very well be uniquely positions for taking a large share of future proof IoT business across USA. Though they are also pretty uniquely position for NB-IoT with their comprehensive 700MHz LTE coverage.
For 5G IoT to be meaningful (at scale) the conventional macro-cellular networks needs to be in play for 5G coverage .,, certainly 100% 5G coverage will be a requirement. Although, even with 5G there maybe 100s of Billion of non-5G IoT devices that require coverage and management.
≤ 500 km/h SERVICE SUPPORT.
Sure why not? but why not faster than that? At hyperloop or commercial passenger airplane speeds for example?
Before we get all excited about Gbps speeds at 500 km/h, it should be clear that the 5G vision paper only proposed speeds between 10 Mbps up-to 50 Mbps (actually it is allowed to regress down to 50 kilo bits per second). With 200 Mbps for broadcast like services.
So in general, this is a pretty reasonable requirement. Maybe with the 200 Mbps for broadcasting services being somewhat head scratching unless the vehicle is one big 16K screen. Although the users proximity to such a screen does not guaranty an ideal 16K viewing experience to say the least.
What moves so fast?
The fastest train today is tracking at ca. 435 km/h (Shanghai Maglev, China).
Typical cruising airspeed for a long-distance commercial passenger aircraft is approx. 900 km/h. So we might not be able to provide the best 5G experience in commercial passenger aircrafts … unless we solve that with an in-plane communications system rather than trying to provide Gbps speed by external coverage means.
Why take a plane when you can jump on the local Hyperloop? The proposed Hyperloop should track at an average speed of around 970 km/h (faster or similar speeds as commercial passengers aircrafts), with a top speed of 1,200 km/h. So if you happen to be in between LA and San Francisco in 2020+ you might not be able to get the best 5G service possible … what a bummer! This is clearly an area where the vision did not look far enough.
Providing services to moving things at a relative fast speed does require a reasonable good coverage. Whether it being train track, hyperloop tunnel or ground to air coverage of commercial passenger aircraft, new coverage solutions would need to be deployed. Or alternative in-vehicular coverage solutions providing a perception of 5G experience might be an alternative that could turn out to be more economical.
The speed requirement is a very reasonable one particular for train coverage.
50% TOTAL NETWORK ENERGY REDUCTION.
If 5G development could come true on this ambition we talk about 10 Billion US Dollars (for the cellular industry). Equivalent to a percentage point on the margin.
There are two aspects of energy efficiency in a cellular based communication system.
User equipment that will benefit from longer intervals without charging and thus improve customers experience and overall save energy from less frequently charges.
Network infrastructure energy consumption savings will directly positively impact a telecom operators Ebitda.
Energy efficient Smartphones
The first aspect of user equipment is addressed by the 5G vision paper under “4.3 Device Requirements” sub-section “4.3.3 Device Power Efficiency”; “Battery life shall be significantly increased: at least 3 days for a smartphone, and up tp 15 years for a low-cost MTC device.”(note: MTC = Machine Type Communications).
Apple’s iPhone 7 battery life (on a full charge) is around 6 hours of constant use with 7 Plus beating that with ca. 3 hours (i.e., total 9 hours). So 3 days will go a long way.
It is however unclear whether the 3 extra days of a 5G smartphone battery life-time is supposed to be under active usage conditions or just in idle mode. Obviously in order to matter materially to the consumer one would expect this vision to apply to active usage (i.e., 4+ hours a day at 100s of Mbps – 1Gbps operations).
Energy efficient network infrastructure.
The 5G vision paper defines energy efficiency as number of bits that can be transmitted over the telecom infrastructure per Joule of Energy.
The total energy cost, i.e., operational expense (OpEx), of telecommunications network can be considerable. Despite our mobile access technologies having become more energy efficient with each generation, the total OpEx of energy attributed to the network infrastructure has increased over the last 10 years in general. The growth in telco infrastructure related energy consumption has been driven by the consumer demand for broadband services in mobile and fixed including incredible increase in data center computing and storage requirements.
In general power consumption OpEx share of total technology cost amounts to 8% to 15% (i.e., for Telcos without heavy reliance of diesel). The general assumption is that with regular modernization, energy efficiency gain in newer electronics can keep growth in energy consumption to a minimum compensating for increased broadband and computing demand.
Note: Technology Opex (including NT & IT) on average lays between 18% to 25% of total corporate Telco Opex. Out of the Technology Opex between 8% to 15% (max) can typically be attributed to telco infrastructure energy consumption. The access & aggregation contribution to the energy cost typically would towards 80% plus. Data centers are expected to increasingly contribute to the power consumption and cost as well. Deep diving into the access equipment power consumption, ca. 60% can be attributed to rectifiers and amplifiers, 15% by the DC power system & miscellaneous and another 25% by cooling.
5G vision paper is very bullish in their requirement to reduce the total energy and its associated cost; it is stated “5G should support a 1,000 times traffic increase in the next 10 years timeframe, with an 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 of x2,000 in the next 10 years timeframe.” (sub-section “4.6.2 Energy Efficiency” NGMN 5G White Paper).
This requirement would mean that in a pure 5G world (i.e., all traffic on 5G), the power consumption arising from the cellular network would be 50% of what is consumed today. In 2016 terms the Mobile-based Opex saving would be in the order of 5 Billion US$ to 10+ Billion US$ annually. This would be equivalent to 0.5% to 1.1% margin improvement globally (note: using GSMA 2016 Revenue & Growth data and Pyramid Research forecast). If energy price would increase over the next 10 years the saving / benefits would of course be proportionally larger.
As we have seen in the above, it is reasonable to expect a very considerable increase in cell density as the broadband traffic demand increases from peak bandwidth (i.e., 1 – 10 Gbps) and traffic density (i.e., 1 Tbps per km2) expectations.
Depending on the demanded traffic density, spectrum and carrier frequency available for 5G between 100 to 1,000 small cell sites per km2 could be required over the next 10 years. This cell site increase will be required in addition to existing macro-cellular network infrastructure.
Today (in 2017) an operator in EU28-sized country may have between ca. 3,500 to 35,000 cell sites with approx. 50% covering rural areas. Many analysts are expecting that for medium sized countries (e.g., with 3,500 – 10,000 macro cellular sites), operators would eventually have up-to 100,000 small cells under management in addition to their existing macro-cellular sites. Most of those 5G small cells and many of the 5G macro-sites we will have over the next 10 years, are also going to have advanced massive MiMo antenna systems with many active antenna elements per installed base antenna requiring substantial computing to gain maximum performance.
It appears with today’s knowledge extremely challenging (to put it mildly) to envision a 5G network consuming 50% of today’s total energy consumption.
It is highly likely that the 5G radio node electronics in a small cell environment (and maybe also in a macro cellular environment?) will consume less Joules per delivery bit (per second) due to technology advances and less transmitted power required (i.e., its a small or smallest cell). However, this power efficiency technology and network cellular architecture gain can very easily be destroyed by the massive additional demand of small, smaller and smallest cells combined with highly sophisticated antenna systems consuming additional energy for their compute operations to make such systems work. Furthermore, we will see operators increasingly providing sophisticated data center resources network operations as well as for the customers they serve. If the speed of light is insufficient for some services or country geographies, additional edge data centers will be introduced, also leading to an increased energy consumption not present in todays telecom networks. Increased computing and storage demand will also make the absolute efficiency requirement highly challenging.
Will 5G be able to deliver bits (per second) more efficiently … Yes!
Will 5G be able to reduce the overall power consumption of todays telecom networks with 50% … highly unlikely.
In my opinion the industry will have done a pretty good technology job if we can keep the existing energy cost at the level of today (or even allowing for unit price increases over the next 10 years).
The Total power reduction of our telecommunications networks will be one of the most important 5G development tasks as the industry cannot afford a new technology that results in waste amount of incremental absolute cost. Great relative cost doesn’t matter if it results in above and beyond total cost.
≥ 99.999% NETWORK AVAILABILITY & DATA CONNECTION RELIABILITY.
A network availability of 5Ns across all individual network elements and over time correspond to less than a second a day downtime anywhere in the network. Few telecom networks are designed for that today.
5 Nines (5N) is a great aspiration for services and network infrastructures. It also tends to be fairly costly and likely to raise the level of network complexity. Although in the 5G world of heterogeneous networks … well its is already complicated.
5N Network Availability.
From a network and/or service availability perspective it means that over the cause of the day, your service should not experience more than 0.86 seconds of downtime. Across a year the total downtime should not be more than 5 minutes and 16 seconds.
The way 5N Network Availability is define is “The network is available for the targeted communications in 99.999% of the locations where the network is deployed and 99.999% of the time”. (from “4.4.4 Resilience and High Availability”, NGMN 5G White Paper).
Thus in a 100,000 cell network only 1 cell is allowed experience a downtime and for no longer than less than a second a day.
It should be noted that there are not many networks today that come even close to this kind of requirement. Certainly in countries with frequent long power outages and limited ancillary backup (i.e., battery and/or diesel) this could be a very costly design requirement. Networks relying on weather-sensitive microwave radios for backhaul or for mm-wave frequencies 5G coverage would be required to design in a very substantial amount of redundancy to keep such high geographical & time availability requirements
In general designing a cellular access network for this kind of 5N availability could be fairly to very costly (i.e., Capex could easily run up in several percentage points of Revenue).
One way out from a design perspective is to rely on hierarchical coverage. Thus, for example if a small cell environment is un-available (=down!) the macro-cellular network (or overlay network) continues the service although at a lower service level (i.e., lower or much lower speed compared to the primary service). As also suggested in the vision paper making use of self-healing network features and other real-time measures are expected to further increase the network infrastructure availability. This is also what one may define as Network Resilience.
Nevertheless, the “NGMN 5G White Paper” allows for operators to define the level of network availability appropriate from their own perspective (and budgets I assume).
5N Data Packet Transmission Reliability.
The 5G vision paper, defines Reliability as “… amount of sent data packets successfully delivered to a given destination, within the time constraint required by the targeted service, divided by the total number of sent data packets.”. (“4.4.5 Reliability” in “NGMN 5G White Paper”).
It should be noted that the 5N specification in particular addresses specific use cases or services of which such a reliability is required, e.g., mission critical communications and ultra-low latency service. The 5G allows for a very wide range of reliable data connection. Whether the 5N Reliability requirement will lead to substantial investments or can be managed within the overall 5G design and architectural framework, might depend on the amount of traffic requiring 5Ns.
The 5N data packet transmission reliability target would impose stricter network design. Whether this requirement would result in substantial incremental investment and cost is likely dependent on the current state of existing network infrastructure and its fundamental design.
If you have read Michael Lewis book “Flash Boys”, I will have absolutely no problem convincing you that a few milliseconds improvement in transport time (i.e., already below 20 ms) of a valuable signal (e.g., containing financial information) can be of tremendous value. It is all about optimizing transport distances, super efficient & extremely fast computing and of course ultra-high availability. The ultra-low transport and process latencies is the backbone (together with the algorithms obviously) of the high frequency trading industry that takes a market share of between 30% (EU) and 50% (US) of the total equity trading volume.
In a recent study by The Boston Consulting Group (BCG) “Uncovering Real Mobile Data Usage and Drivers of Customer Satisfaction” (Nov. 2015) study it was found that latency had a significant impact on customer video viewing satisfaction. For latencies between 75 – 100 milliseconds 72% of users reported being satisfied. The user experience satisfaction level jumped to 83% when latency was below 50 milliseconds. We have most likely all experienced and been aggravated by long call setup times (> couple of seconds) forcing us to look at the screen to confirm that a call setup (dialing) is actually in progress.
Latency and reactiveness or responsiveness matters tremendously to the customers experience and whether it is a bad, good or excellent one.
The Tactile Internet idea is an integral part of the “NGMN 5G Vision” and part of what is characterized as Extreme Real-Time Communications. It has further been worked out in detail in the ITU-T Technology Watch Report “The Tactile Internet” from August 2014.
The word “Tactile” means perceptible by touch. It closely relates to the ambition of creating a haptic experience. Where haptic means a sense of touch. Although we will learn that the Tactile Internet vision is more than a “touchy-feeling” network vision, the idea of haptic feedback in real-time (~ sub-millisecond to low millisecond regime) is very important to the idea of a Tactile Network experience (e.g., remote surgery).
The Tactile Internet is characterized by
Ultra-low latency; 1 ms and below latency (as in round-trip-time / round-trip delay).
Ultra-high availability; 99.999% availability.
Ultra-secure end-2-end communications.
Persistent very high bandwidths capability; 1 Gbps and above.
The Tactile Internet is one of the corner stones of 5G. It promises ultra-low end-2-end latencies in the order of 1 millisecond at Giga bits per second speeds and with five 9’s of availability (translating into a 500 ms per day average un-availability).
Interestingly, network predictability and variation in latency have not been receiving too much focus within the Tactile Internet work. Clearly, a high degree of predictability as well as low jitter (or latency variation), could be very desirable property of a tactile network. Possibly even more so than absolute latency in its own right. A right sized round-trip-time with imposed managed latency, meaning a controlled variation of latency, is very essential to the 5G Tactile Internet experience.
It’s 5G on speed and steroids at the same time.
Let us talk about the elephant in the room.
We can understand Tactile latency requirements in the following way;
An Action including (possible) local Processing, followed by some Transport and Remote Processing of data representing the Action, results in a Re-action again including (possible) local Processing. According with Tactile Internet Vision, the time of this whole even from Action to Re-action has to have run its cause within 1 millisecond or one thousand of a second. In many use cases this process is looped as the Re-action feeds back, resulting in another action. Note in the illustration below, Action and Re-action could take place on the same device (or locality) or could be physically separated. The processes might represent cloud-based computations or manipulations of data or data manipulations local to the device of the user as well as remote devices. It needs to be considered that the latency time scale for one direction is not at all given to be the same in the other direction (even for transport).
The simplest example is the mouse click on a internet link or URL (i.e., the Action) resulting a translation of the URL to an IP address and the loading of the resulting content on your screen (i.e., part of the process) with the final page presented on the your device display (i.e., Re-action). From the moment the URL is mouse-clicked until the content is fully presented should take no longer than 1 ms.
A more complex use case might be remote surgery. In which a surgical robot is in one location and the surgeon operator is at another location manipulating the robot through an operation. This is illustrated in the above picture. Clearly, for a remote surgical procedure to be safe (i.e., within the margins of risk of not having the possibility of any medical assisted surgery) we would require a very reliable connection (99.999% availability), sufficient bandwidth to ensure adequate video resolution as required by the remote surgeon controlling the robot, as little as possible latency allowing the feel of instantaneous (or predictable) reaction to the actions of the controller (i.e., the surgeons) and of course as little variation in the latency (i.e., jitter) allowing system or human correction of the latency (i.e., high degree of network predictability).
The first Complete Trans-Atlantic Robotic Surgery happened in 2001. Surgeons in New York (USA) remotely operated on a patient in Strasbourg, France. Some 7,000 km away or equivalent to 70 ms in round-trip-time (i.e., 14,000 km in total) for light in fiber. The total procedural delay from hand motion (action) to remote surgical response (reaction) showed up on their video screen took 155 milliseconds. From trials on pigs any delay longer than 330 ms was thought to be associated with an unacceptable degree of risk for the patient. This system then did not offer any haptic feedback to the remote surgeon. This remains the case for most (if not all) remote robotic surgical systems in option today as the latency in most remote surgical scenarios render haptic feedback less than useful. An excellent account for robotic surgery systems (including the economics) can be found at this web site “All About Robotic Surgery”. According to experienced surgeons at 175 ms (and below) a remote robotic operation is perceived (by the surgeon) as imperceptible.
It should be clear that apart from offering long-distance surgical possibilities, robotic surgical systems offers many other benefits (less invasive, higher precision, faster patient recovery, lower overall operational risks, …). In fact most robotic surgeries are done with surgeon and robot being in close proximity.
Another example of coping with lag or latency is a Predator drone pilot. The plane is a so-called unmanned combat aerial vehicle and comes at a price of ca. 4 Million US$ (in 2010) per piece. Although this aerial platform can perform missions autonomously it will typically have two pilots on the ground monitoring and possible controlling it. The typical operational latency for the Predator can be as much as 2,000 milliseconds. For takeoff and landing, where this latency is most critical, typically the control is handed to to a local crew (either in Nevada or in the country of its mission). The Predator cruise speed is between 130 and 165 km per hour. Thus within the 2 seconds lag the plane will have move approximately 100 meters (i.e., obviously critical in landing & take off scenarios). Nevertheless, a very high degree of autonomy has been build into the Predator platform that also compensates for the very large latency between plane and mission control.
Back to the Tactile Internet latency requirements;
In LTE today, the minimum latency (internal to the network) is around 12 ms without re-transmission and with pre-allocated resources. However, the normal experienced latency (again internal to the network) would be more in the order of 20 ms including 10% likelihood of retransmission and assuming scheduling (which would be normal). However, this excludes any content fetching, processing, presentation on the end-user device and the transport path beyond the operators network (i.e., somewhere in the www). Transmission outside the operator network typically between 10 and 20 ms on-top of the internal latency. The fetching, processing and presentation of content can easily add hundreds of milliseconds to the experience. Below illustrations provides a high level view of the various latency components to be considered in LTE with the transport related latencies providing the floor level to be expected;
In 5G the vision is to achieve a factor 20 better end-2-end (within the operators own network) round-trip-time compared to LTE; thus 1 millisecond.
So … what happens in 1 millisecond?
Light will have travelled ca. 200 km in fiber or 300 km in free-space. A car driving (or the fastest baseball flying) 160 km per hour will have moved 4 cm. A steel ball falling to the ground (on Earth) would have moved 5 micro meter (that’s 5 millionth of a meter). In a 1Gbps data stream, 1 ms correspond to ca. 125 Kilo Bytes worth of data. A human nerve impulse last just 1 ms (i.e., in a 100 millivolt pulse).
It should be clear that the 1 ms poses some very dramatic limitations;
The useful distance over which a tactile applications would work (if 1 ms would really be the requirements that is!) will be short ( likely a lot less than 100 km for fiber-based transport)
The air-interface (& number of control plane messages required) needs to reduce dramatically from milliseconds down to microseconds, i.e., factor 20 would require no more than 100 microseconds limiting the useful cell range).
Compute & processing requirements, in terms of latency, for UE (incl. screen, drivers, local modem, …), Base Station and Core would require a substantial overhaul (likely limiting level of tactile sophistication).
Require own controlled network infrastructure (at least a lot easier to manage latency within), avoiding any communication path leaving own network (walled garden is back with a vengeance?).
Network is the sole responsible for latency and can be made arbitrarily small (by distance and access).
Very small cells, very close to compute & processing resources, would be most likely candidates for fulfilling the tactile internet requirements.
Thus instead of moving functionality and compute up and towards the cloud data center we (might) have an opposing force that requires close proximity to the end-users application. Thus, the great promise of cloud-based economical efficiency is likely going to be dented in this scenario by requiring many more smaller data centers and maybe even micro-data centers moving closer to the access edge (i.e., cell site, aggregation site, …). Not surprisingly, Edge Cloud, Edge Data Center, Edge X is really the new Black …The curse of the edge!?
Looking at several network and compute design considerations a tactile application would require no more than 50 km (i.e., 100 km round-trip) effective round-trip distance or 0.5 ms fiber transport (including switching & routing) round-trip-time. Leaving another 0.5 ms for air-interface (in a cellular/wireless scenario), computing & processing. Furthermore, the very high degree of imposed availability (i.e., 99.999%) might likewise favor proximity between the Tactile Application and any remote Processing-Computing. Obviously,
So in all likelihood we need processing-computing as near as possible to the tactile application (at least if one believes in the 1 ms and about target).
One of the most epic (“in the Dutch coffee shop after a couple of hours category”) promises in “The Tactile Internet” vision paper is the following;
“Tomorrow, using advanced tele-diagnostic tools, it could be available anywhere, anytime; allowing remote physical examination even by palpation (examination by touch). The physician will be able to command the motion of a tele-robot at the patient’s location and receive not only audio-visual information but also critical haptic feedback.” (page 6, section 3.5).
Markus Rank et al did systematic research on the perception of delay in haptic tele-presence systems (Presence, October 2010, MIT Press) and found haptic delay detection thresholds between 30 and 55 ms. Thus haptic feedback did not appear to be sensitive to delays below 30 ms, fairly close to the lowest reported threshold of 20 ms. This combined with experienced tele-robotic surgeons assessing that below 175 ms the remote procedure starts to be perceived as imperceptible, might indicate that the 1 ms, at least for this particular use case, is extremely limiting.
The extreme case would be to have the tactile-related computing done at the radio base station assuming that the tactile use case could be restricted to the covered cell and users supported by that cell. I name this the micro-DC (or micro-cloud or more like what some might call the cloudlet concept) idea. This would be totally back to the older days with lots of compute done at the cell site (and likely kill any traditional legacy cloud-based efficiency thinking … love to use legacy and cloud in same sentence). This would limit the round-trip-time to air-interface latency and compute/processing at the base station and the device supporting the tactile application.
It is normal to talk about the round-trip-time between an action and the subsequent reaction. It is also the time it takes a data or signal to travel from a specific source to a specific destination and back again (i.e., round trip). In case of light in fiber, a 1 millisecond limit on the round-trip-time would imply that the maximum distance that can be travelled (in the fiber) between source to destination and back to the source is 200 km. Limiting the destination to be no more than 100 km away from the source. In case of substantial processing overhead (e.g., computation) the distance between source and destination requires even less than 100 km to allow for the 1 ms target.
THE HUMAN SENSES AND THE TACTILE INTERNET.
The “touchy-feely” aspect, or human sensing in general, is clearly an inspiration to the authors of “The Tactile Internet” vision as can be seen from the following quote;
“We experience interaction with a technical system as intuitive and natural only if the feedback of the system is adapted to our human reaction time. Consequently, the requirements for technical systems enabling real-time interactions depend on the participating human senses.” (page 2, Section 1).
The following human-reaction times illustration shown below is included in “The Tactile Internet” vision paper. Although it originates from Fettweis and Alamouti’s paper titled “5G: Personal Mobile Internet beyond What Cellular Did to Telephony“. It should be noted that the description of the Table is order of magnitude of human reaction times; thus, 10 ms might also be 100 ms or 1 ms and so forth and therefor, as we shall see, it would be difficult to a given reaction time wrong within such a range.
The important point here is that the human perception or senses impact very significantly the user’s experience with a given application or use case.
The responsiveness of a given system or design is incredible important for how well a service or product will be perceived by the user. The responsiveness can be defined as a relative measure against our own sense or perception of time. The measure of responsiveness is clearly not unique but depends on what senses are being used as well as the user engaged.The human mind is not fond of waiting and waiting too long causes distraction, irritation and ultimate anger after which the customer is in all likelihood lost. A very good account of considering the human mind and it senses in design specifications (and of course development) can be found in Jeff Johnson’s 2010 book “Designing with the Mind in Mind”.
The understanding of human senses and the neurophysiological reactions to those senses are important for assessing a given design criteria’s impact on the user experience. For example, designing for 1 ms or lower system reaction times when the relevant neurophysiological timescale is measured in 10s or 100s of milliseconds is likely not resulting in any noticeable (and monetizable) improvement in customer experience. Of course there can be many very good non-human reasons for wanting low or very low latencies.
While you might get the impression, from the above table above from Fettweis et al and countless Tactile Internet and 5G publications referring back to this data, that those neurophysiological reactions are natural constants, it is unfortunately not the case. Modality matters hugely. There are fairly great variations in reactions time within the same neurophysiological response category depending on the individual human under test but often also depending on the underlying experimental setup. In some instances the reaction time deduced would be fairly useless as a design criteria for anything as the detection happens unconsciously and still require the relevant part of the brain to make sense of the event.
Based on IAAF (International Athletic Association Federation) rules, an athlete is deemed to have had a false start if that athlete moves sooner than 100 milliseconds after the start signal. The neurophysiological process relevant here is the neuromuscular reaction to the sound heard (i.e., the big bang of the pistol) by the athlete. Research carried out by Paavo V. Komi et al has shown that the reaction time of a prepared (i.e., waiting for the bang!) athlete can be as low as 80 ms. This particular use case relates to the auditory reaction times and the subsequent physiological reaction. P.V. Komi et al also found a great variation in the neuromuscular reaction time to the sound (even far below the 80 ms!).
Neuromuscular reactions to unprepared events typically typically measures in several hundreds of milliseconds (up-to 700 ms) being somewhat faster if driven by auditory senses rather than vision. Note that reflex time scales are approximately 10 times faster or in the order of 80 – 100 ms.
The international Telecommunications Union (ITU) Recommendation G.114, defines for voice applications an upper acceptable one-way (i.e., its you talking you don’t want to be talked back to by yourself) delay of 150 ms. Delays below this limit would provide an acceptable degree of voice user experience in the sense that most users would not hear the delay. It should be understood that a great variation in voice delay sensitivity exist across humans. Voice conversations would be perceived as instantaneous by most below the 100 ms (thought the auditory perception would also depend on the intensity/volume of the voice being listened to).
Finally, let’s discuss human vision. Fettweis et al in my opinion mixes up several psychophysical concepts of vision and TV specifications. Alluding to 10 millisecond is the visual “reaction” time (whatever that now really means). More accurately they describe the phenomena of flicker fusion threshold which describes intermittent light stimulus (or flicker) is perceived as completely steady to an average viewer. This phenomena relates to persistence of vision where the visual system perceives multiple discrete images as a single image (both flicker and persistence of vision are well described in both by Wikipedia and in detail by Yhong-Lin Lu el al “Visual Psychophysics”). There, are other reasons why defining flicker fusion and persistence of vision as a human reaction reaction mechanism is unfortunate.
The 10 ms for vision reaction time, shown in the table above, is at the lowest limit of what researchers (see references 14, 15, 16 ..) find to be the early stages of vision can possible detect (i.e., as opposed to pure guessing ). Mary C. Potter of M.I.T.’s Dept. of Brain & Cognitive Sciences, seminal work on human perception in general and visual perception in particular shows that the human vision is capable very rapidly to make sense of pictures, and objects therein, on the timescale of 10 milliseconds (i.e., 13 ms actually is the lowest reported by Potter). From these studies it is also found that preparedness (i.e., knowing what to look for) helps the detection process although the overall detection results did not differ substantially from knowing the object of interest after the pictures were shown. Note that the setting of these visual reaction time experiments all happens in a controlled laboratory setting with the subject primed to being attentive (e.g., focus on screen with fixation cross for a given period, followed by blank screen for another shorter period, and then a sequence of pictures each presented for a (very) short time, followed again by a blank screen and finally a object name and the yes-no question whether the object was observed in the sequence of pictures). Often these experiments also includes a certain degree of training before the actual experiment took place. The relevant memory of the target object, In any case and unless re-enforced, will rapidly dissipates. in fact the shorter the viewing time, the quicker it will disappear … which might be a very healthy coping mechanism.
To call this visual reaction time of 10+ ms typical is in my opinion a bit of a stretch. It is typical for that particular experimental setup and very nicely provides important insights into the visual systems capabilities.
One of the more silly things used to demonstrate the importance of ultra-low latencies have been to time delay the video signal send to a wearer’s goggles and then throw a ball at him in the physical world … obviously, the subject will not catch the ball (might as well as thrown it at the back of his head instead). In the Tactile Internet vision paper it the following is stated; “But if a human is expecting speed, such as when manually controlling a visual scene and issuing commands that anticipate rapid response, 1-millisecond reaction time is required” (on page 3). And for the record spinning a basketball on your finger has more to do with physics than neurophysiology and human reaction times.
In more realistic settings it would appear that the (prepared) average reaction time of vision is around or below 40 ms. With this in mind, a baseball moving (when thrown by a power pitcher) at 160 km per hour (or ca. 4+ cm per ms) would take a approx. 415 ms to reach the batter (using an effective distance of 18.44 meters). Thus the batter has around 415 ms to visually process the ball coming and hit it at the right time. Given the latency involved in processing vision the ball would be at least 40 cm (@ 10 ms) closer to the batter than his latent visionary impression would imply. Assuming that the neuromuscular reaction time is around 100±20 ms, the batter would need to compensate not only for that but also for his vision process time in order to hit the ball. Based on batting statistics, clearly the brain does compensate for its internal latencies pretty well. In the paper “Human time perception and its illusions” D.M. Eaglerman that the visual system and the brain (note: visual system is an integral part of the brain) is highly adaptable in recalibrating its time perception below the sub-second level.
It is important to realize that in literature on human reaction times, there is a very wide range of numbers for supposedly similar reaction use cases and certainly a great deal of apparent contradictions (though the experimental frameworks often easily accounts for this).
The supporting data for the numbers shown in the above figure can be found via the hyperlink in the above text or in the references below.
Thus, in my opinion, also supported largely by empirical data, a good latency E2E design target for a Tactile network serving human needs, would be between 20 milliseconds and 10 milliseconds. With the latency budget covering the end user device (e.g., tablet, VR/AR goggles, IOT, …), air-interface, transport and processing (i.e., any computing, retrieval/storage, protocol handling, …). It would be unlikely to cover any connectivity out of the operator”s network unless such a connection is manageable from latency and jitter perspective though distance would count against such a strategy.
This would actually be quiet agreeable from a network perspective as the distance to data centers would be far more reasonable and likely reduce the aggressive need for many edge data centers using the below 10 ms target promoted in the Tactile Internet vision paper.
There is however one thing that we are assuming in all the above. It is assumed that the user’s local latency can be managed as well and made almost arbitrarily small (i.e., much below 1 ms). Hardly very reasonable even in the short run for human-relevant communications ecosystems (displays, goggles, drivers, etc..) as we shall see below.
For a gaming environment we would look at something like the below illustration;
Lets ignore the use case of local games (i.e., where the player only relies on his local computing environment) and focus on games that rely on a remote gaming architecture. This could either be relying on a client-server based architecture or cloud gaming architecture (e.g., typical SaaS setup). In general the the client-server based setup requires more performance of the users local environment (e.g., equipment) but also allows for more advanced latency compensating strategies enhancing the user perception of instantaneous game reactions. In the cloud game architecture, all game related computing including rendering/encoding (i.e., image synthesis) and video output generation happens in the cloud. The requirements to the end users infrastructure is modest in the cloud gaming setup. However, applying latency reduction strategies becomes much more challenging as such would require much more of the local computing environment that the cloud game architecture tries to get away from. In general the network transport related latency would be the same provide the dedicated game servers and the cloud gaming infrastructure would reside within the same premises. In Choy et al’s 2012 paper “The Brewing Storm in Cloud Gaming: A Measurement Study on Cloud to End-User Latency” , it is shown, through large scale measurements, that current commercial cloud infrastructure architecture is unable to deliver the latency performance for an acceptable (massive) multi-user experience. Partly simply due to such cloud data centers are too far away from the end user. Moreover, the traditional commercial cloud computing infrastructure is simply not optimized for online gaming requiring augmentation of stronger computing resources including GPUs and fast memory designs. Choy et al do propose to distribute the current cloud infrastructure targeting a shorter distance between end user and the relevant cloud game infrastructure. Similar to what is already happening today with content distribution networks (CDNs) being distributed more aggressively in metropolitan areas and thus closer to the end user.
A comprehensive treatment on latencies, or response time scales, in games and how these relates to user experience can be found in Kjetil Raaen’s Ph.D. thesis “Response time in games: Requirements and improvements” as well as in the comprehensive relevant literature list found in this thesis.
From the many studies (as found in Raaen’s work, the work of Mark Claypool and much cited 2002 study by Pantel et al) on gaming experience, including massive multi-user online game experience, shows that players starts to notice delay of about 100 ms of which ca. 20 ms comes from play-out and processing delay. Thus, quiet a far cry from the 1 millisecond. From the work, and not that surprising, sensitivity to gaming latency depends on the type of game played (see the work of Claypool) and how experienced a gamer is with the particular game (e.g., Pantel er al). It should also be noted that in a VR environment, you would want to the image that arrives at your visual system to be in synch with your heads movement and the directions of your vision. If there is a timing difference (or lag) between the direction of your vision and the image presented to your visual system, the user experience becomes rapidly poor causing discomfort by disorientation and confusion (possible leading to a physical reaction such as throwing up). It is also worth noting that in VR there is a substantially latency component simple from the image rendering (e.g., 60 MHz frame rate provides a new frame on average every 16.7 millisecond). Obviously chunking up the display frame rate will reduce the rendering related latency. However, several latency compensation strategies (to compensate for you head and eye movements) have been developed to cope with VR latency (e.g., time warping and prediction schemes).
Anyway, if you would be of the impression that VR is just about showing moving images on the inside of some awesome goggles … hmmm do think again and keep dreaming of 1 millisecond end-2end network centric VR delivery solutions (at least for the networks we have today). Of course 1 ms target is possible really a Proxima-Centauri shot as opposed to a just moonshot.
With a target of no more than 20 milliseconds lag or latency and taking into account the likely reaction time of the users VR system (future system!), that likely leaves no more (and likely less) than 10 milliseconds for transport and any remote server processing. Still this could allow for a data center to be 500 km (5 ms round.trip time in fiber) away from the user and allow another 5 ms for data center processing and possible routing delay along the way.
One might very well be concerned about the present Tactile Internet vision and it’s focus on network centric solutions to the very low latency target of 1 millisecond. The current vision and approach would force (fixed and mobile) network operators to add a considerable amount of data centers in order to get the physical transport time down below the 1 millisecond. This in turn drives the latest trend in telecommunication, the so-called edge data center or edge cloud. In the ultimate limit, such edge data centers (however small) might be placed at cell site locations or fixed network local exchanges or distribution cabinets.
Furthermore, the 1 millisecond as a goal might very well have very little return on user experience (UX) and substantial cost impact for telecom operators. A diligent research through academic literature and wealth of practical UX experiments indicates that this indeed might be the case.
Such a severe and restrictive target as the 1 millisecond is, it severely narrows the Tactile Internet to scenarios where sensing, acting, communication and processing happens in very close proximity of each other. In addition the restrictions to system design it imposes, further limits its relevance in my opinion. The danger is, with the expressed Tactile vision, that too little academic and industrious thinking goes into latency compensating strategies using the latest advances in machine learning, virtual reality development and computational neuroscience (to name a few areas of obvious relevance). Further network reliability and managed latency, in the sense of controlling the variation of the latency, might be of far bigger importance than latency itself below a certain limit.
So if 1 ms is no use to most men and beasts … why bother with this?
While very low latency system architectures might be of little relevance to human senses, it is of course very likely (as it is also pointed out in the Tactile Internet Vision paper) that industrial use cases could benefit from such specifications of latency, reliability and security.
For example in machine-to-machine or things-to-things communications between sensors, actuators, databases, and applications very short reaction times in the order of sub-milliseconds to low milliseconds could be relevant.
We will look at this next.
THE TACTILE INTERNET USE CASES & BUSINESS MODELS.
An open mind would hope that most of what we do strives to out perform human senses, improve how we deal with our environment and situations that are far beyond mere mortal capabilities. Alas I might have read too many Isaac Asimov novels as a kid and young adult.
In particular where 5G has its present emphasis of ultra-high frequencies (i.e., ultra small cells), ultra-wide spectral bandwidth (i.e., lots of Gbps) together with the current vision of the Tactile Internet (ultra-low latencies, ultra-high reliability and ultra-high security), seem to be screaming for being applied to Industrial facilities, logistic warehouses, campus solutions, stadiums, shopping malls, tele-, edge-cloud, networked robotics, etc… In other words, wherever we have a happy mix of sensors, actuators, processors, storage, databases and software based solutions across a relative confined area, 5G and the Tactile Internet vision appears to be a possible fit and opportunity.
In the following it is important to remember;
1 ms round-trip time ~ 100 km (in fiber) to 150 km (in free space) in 1-way distance from the relevant action if only transport distance mattered to the latency budget.
Considering the total latency budget for a 1 ms Tactile application the transport distance is likely to be no more than 20 – 50 km or less (i.e., right at the RAN edge).
5G will bring lower latency, compared to an even optimized LTE system, that in a similar setup as the above described for Ocado, could further increase the performance. Obviously very high network reliability promised by 5G of such a logistic system needs to be very high to reduce the risk of disruption and subsequent customer dissatisfaction of late (or no) delivery as well as the exposure to grocery stock turning bad.
This all done within the confines of a warehouse building.
ROBOTICS AND TACTILE CONDITIONS
First of all lets limit the Robotics discussion to use cases related to networked robots. After all if the robot doesn’t need a network (pretty cool) it pretty much a singleton and not so relevant for the Tactile Internet discussion. In the following I am using the word Cloud in a fairly loose way and means any form of computing center resources either dedicated or virtualized. The cloud could reside near the networked robotic systems as well as far away depending on the overall system requirements to timing and delay (e.g., that might also depend on the level of robotic autonomy).
Getting networked robots to work well we need to solve a host of technical challenges, such as
Jitter (i.e., variation of latency).
Robot-2-ROS (i.e., general robotics operations system).
Power budget (e.g., power limitations, re-charging).
Sensor & actuator fusion (e.g., consolidate & align data from distributed sources for example sensor-actuator network).
Autonomy vs human control.
Machine learning / machine intelligence.
Safety (e.g., human and non-human).
Security (e.g., against cyber threats).
The network connection-part of the networked robotics system can be either wireless, wired, or a combination of wired & wireless. Connectivity could be either to a local computing cloud or data center, to an external cloud (on the internet) or a combination of internal computing for control and management for applications requiring very low-latency very-low jitter communications and external cloud for backup and latency-jitter uncritical applications and use cases.
For connection types we have Wired (e.g., LAN), Wireless (e.g., WLAN) and Cellular (e.g., LTE, 5G). There are (at least) three levels of connectivity we need to consider; inter-robot communications, robot-to-cloud communications (or operations and control systems residing in Frontend-Cloud or computing center), and possible Frontend-Cloud to Backend-Cloud (e..g, for backup, storage and latency-insensitive operations and control systems). Obviously, there might not be a need for a split in Frontend and Backend Clouds and pending on the use case requirements could be one and the same. Robots can be either stationary or mobile with a need for inter-robot communications or simply robot-cloud communications.
Various networked robot connectivity architectures are illustrated below;
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
“Neurophysiology: A Conceptual Approach” by Roger Carpenter & Benjamin Reddi (Fifth Edition, 2013 CRC Press).Definitely a very worthy read by anyone who want to understand the underlying principles of sensory functions and basic neural mechanisms.
“Designing with the Mind in Mind” by Jeff Johnson (2010, Morgan Kaufmann). Lots of cool information of how to design a meaningful user interface and of basic user expirence principles worth thinking about.
“World first in radio design” by Cambridge Consultants. Describing the work Cambridge Consultants did with Ocado (UK-based) to design the worlds most automated technologically advanced warehouse based on 4G connected robotics. Please do see the video enclosed in page.
“Ocado: next-generation warehouse automation” by Cambridge Consultants.
After 3G came 4G. After 4G comes 5G. After 5G comes 6G. The Shrivatsa of Technology.
This blog (over the next months a series of Blogs dedicated to 5G), “5G Economics – An Introduction”, has been a very long undertaking. In the making since 2014. Adding and then deleting as I change my opinion and then changed it again. The NGNM Alliance “NGMN 5G White Paper” (here after the NGMN whitepaper) by Rachid El Hattachi & Javan Erfanian has been both a source of great visionary inspiration as well as a source of great worry when it comes to the economical viability of their vision. Some of the 5G ideas and aspirations are truly moonshot in nature and would make the Singularity University very proud.
So what is the 5G Vision?
“5G is an end-to-end ecosystem to enable a fully mobile and connected society. It empowers value creation towards customers and partners, through existing and emerging use cases, delivered with consistent experience, and enabled by sustainable business models.” (NGMN 5G Vision, NGMN 5G whitepaper).
The NGMN 5G vision is not only limited to enhancement of the radio/air interface (although it is the biggest cost & customer experience factor). 5G seeks to capture the complete end-2-end telecommunications system architecture and its performance specifications. This is an important difference from past focus on primarily air interface improvements (e.g., 3G, HSPA, LTE, LTE-adv) and relative modest evolutionary changes to the core network architectural improvements (PS CN, EPC). In particular, the 5G vision provides architectural guidance on the structural separation of hardware and software. Furthermore, it utilizes the latest development in software defined telecommunications functionality enabled by cloudification and virtualization concepts known from modern state-of-the art data centers. The NGMN 5G vision most likely have accepted more innovation risk than in the past as well as being substantially more ambitious in both its specifications and the associated benefits.
“To boldly go where no man has gone before”
In the following, I encourage the reader to always keep in the back of your mind; “It is far easier to criticize somebody’s vision, than it is to come with the vision yourself”. I have tons of respect for the hard and intense development work, that so far have been channeled into making the original 5G vision into a deployable technology that will contribute meaningfully to customer experience and the telecommunications industry.
For much of the expressed concerns in this blog and in other critiques, it is not that those concerns have not been considered in the NGMN whitepaper and 5G vision, but more that those points are not getting much attention.
The cellular “singularity”, 5G that is, is supposed to hit us by 2020. In only four years. Americans and maybe others, taking names & definitions fairly lightly, might already have “5G” ( a l’Americaine) in a couple of years before the real thing will be around.
The 5G Vision is a source of great inspiration. The 5G vision will (and is) requiring a lot of innovation efforts, research & development to actually deliver on what for most parts are very challenging improvements over LTE.
My own main points of concern are in particular towards the following areas;
Obsession with very high sustainable connection throughputs (> 1 Gbps).
Extremely low latencies (1 ms and below).
Too little (to none) focus on controlling latency variation (e.g., jitter), which might be of even greater importance than very low latency (<<10 ms) in its own right. I term this network predictability.
Too strong focus on frequencies above 3 GHz in general and in particular the millimeter wave range of 30 GHz to 300 GHz.
Backhaul & backbone transport transformation needed to support the 5G quantum leap in performance has been largely ignored.
Relative weak on fixed – mobile convergence.
Not so much whether some of the above points are important or not .. they are of course important. Rather it is a question of whether the prioritization and focus is right. A question of channeling more efforts into very important (IMO) key 5G success factors, e.g., transport, convergence and designing 5G for the best user experience (and infinitely faster throughput per user is not the answer) ensuring the technology to be relevant for all customers and not only the ones who happens to be within coverage of a smallest cell.
Not surprisingly the 5G vision is a very mobile system centric. There is too little attention to fixed-mobile convergence and the transport solutions (backhaul & backbone) that will enable the very high air-interface throughputs to be carried through the telecoms network. This is also not very surprising as most mobile folks, historically did not have to worry too much about transport at least in mature advanced markets (i.e., the solutions needed was there without innovation an R&D efforts).
However, this is a problem. The required transport upgrade to support the 5G promises is likely to be very costly. The technology economics and affordability aspects of what is proposed is still very much work in progress. It is speculated that new business models and use cases will be enabled by 5G. So far little has been done in quantifying those opportunities and see whether those can justify some of the incremental cost that surely operators will incur as the deploy 5G.
CELLULAR CAPACITY … IT WORKS FOR 5G TOO!
To create more cellular capacity measured in throughput is easy or can be made so with a bit of approximations. “All” we need is an amount of frequency bandwidth Hz, an air-interface technology that allow us to efficiently carry a certain amount of information in bits per second per unit bandwidth per capacity unit (i.e., we call this spectral efficiency) and a number of capacity units or multipliers which for a cellular network is the radio cell. The most challenging parameter in this game is the spectral efficiency as it is governed by the laws of physics with a hard limit (actually silly me … bandwidth and capacity units are obviously as well), while a much greater degree of freedom governs the amount of bandwidth and of course the number of cells.
Spectral efficiency is given by the so-called Shannon’s Law (for the studious inclined I recommend to study his 1948 paper “A Mathematical Theory of Communications”). The consensus is that we are very close to the Shannon Limit in terms of spectral efficiency (in terms of bits per second per Hz) of the cellular air-interface itself. Thus we are dealing with diminishing returns of what can be gained by further improving error correction, coding and single-input single-output (SISO) antenna technology.
I could throw more bandwidth at the capacity problem (i.e., the reason for the infatuation with the millimeter wave frequency range as there really is a lot available up there at 30+ GHz) and of course build a lot more cell sites or capacity multipliers (i.e., definitely not very economical unless it results in a net positive margin). Of course I could (and most likely will if I had a lot of money) do both.
I could also try to be smart about the spectral efficiency and Shannon’s law. If I could reduce the need for or even avoid building more capacity multipliers or cell sites, by increasing my antenna system complexity it is likely resulting in very favorable economics. It turns out that multiple antennas acts as a multiplier (simplistic put) for the spectral efficiency compared to a simple single (or legacy) antenna system. Thus, the way to improve the spectral efficiency inevitable leads us to substantially more complex antenna technologies (e.g., higher order MiMo, massive MiMo, etc…).
Building new cell sites or capacity multiplier should always be the last resort as it is most likely the least economical option available to boost capacity.
Thus we should be committing increasingly more bandwidth (i.e., 100s – 1000s of Mhz and beyond) assuming it is available (i.e, if not we are back to adding antenna complexity and more cell sites). The need for very large bandwidths, in comparison with what is deployed in today’s cellular systems, automatically forces the choices into high frequency ranges, i.e., >3 GHz and into the millimeter wave range of above 30 GHz. The higher frequency band leads in inevitably to limited coverage and a high to massive demand for small cell deployment.
Yes! It’s a catch 22 if there ever was one. The higher carrier frequency increases the likelihood of more available bandwidth. higher carrier frequency also results in a reduced the size of our advanced complex antenna system (which is good). Both boost capacity to no end. However, my coverage area where I have engineered the capacity boost reduces approx. with the square of the carrier frequency.
Clearly, ubiquitous 5G coverage at those high frequencies (i.e., >3 GHz) would be a very silly endeavor (to put it nicely) and very un-economical.
5G, as long as the main frequency deployed is in the high or very high frequency regime, would remain a niche technology. Irrelevant to a large proportion of customers and use cases.
5G needs to be macro cellular focused to become relevant for all customers and economically beneficial to most use cases.
THE CURIOUS CASE OF LATENCY.
The first time I heard about the 5G 1 ms latency target (communicated with a straight face and lots of passion) was to ROFL. Not a really mature reaction (mea culpa) and agreed, many might have had the same reaction when J.F. Kennedy announced to put a man on the moon and safely back on Earth within 10 years. So my apologies for having had a good laugh (likely not the last to laugh though in this matter).
In Europe, the average LTE latency is around 41±9 milliseconds including pinging an external (to the network) server but does not for example include the additional time it takes to load a web page or start a video stream. The (super) low latency (1 ms and below) poses other challenges but at least relevant to the air-interface and a reasonable justification to work on a new air-interface (apart from studying channel models in the higher frequency regime). The best latency, internal to the mobile network itself, you can hope to get out of “normal” LTE as it is commercially deployed is slightly below 20 ms (without considering re-transmission). For pre-allocated LTE this can further be reduced towards the 10 ms (without considering re-transmission which adds at least 8 ms). In 1 ms light travels ca. 200 km (in optical fiber). To support use cases requiring 1 ms End-2-End latency, all transport & processing would have to be kept inside the operators network. Clearly, the physical transport path to the location, where processing of the transported data would occur, would need to be very short to guaranty 1 ms. The relative 5G latency improvement over LTE would need to be (much) better than 10 (LTE pre-allocated) to 20 times (scheduled “normal” LTE), ignoring re-transmission (which would only make the challenge bigger.
An example. Say that 5G standardization folks gets the latency down to 0.5 ms (vs the ~ 20 – 10 ms today), the 5G processing node (i.e., Data Center) cannot be more than 50 km away from the 5G-radio cell (i..e, it takes light ca. 0.5 ms travel 100 km in fiber). This latency (budget) challenge has led the Telco industry to talk about the need for so-called edge computing and the need for edge data centers to provide the 5G promise of very low latencies. Remember this is opposing the past Telco trend of increasing centralization of computing & data processing resources. Moreover, it is bound to lead to incremental cost. Thus, show me the revenues.
There is no doubt that small, smaller and smallest 5G cells will be essential for providing the very lowest latencies and the smallness is coming for “free” given the very high frequencies planned for 5G. The cell environment of a small cell is more ideal than a macro-cellular harsh environment. Thus minimizing the likelihood of re-transmission events. And distances are shorter which helps as well.
I believe that converged telecommunications operators, are in a better position (particular compared to mobile only operations) to leverage existing fixed infrastructure for a 5G architecture relying on edge data centers to provide very low latencies. However, this will not come for free and without incremental costs.
End-2-End latency in the order of 20 ms are very important for a solid high quality VR user experience. However, to meet this kind of performance figure the VR content needs to be within the confines for the operator’s own network boundaries.
End-2-End (E2E) latencies of less than 100 ms would in general be perceived as instantaneous for normal internet consumption (e.g., social media, browsing, …). However that this still implies that operators will have to focus on developing internal to their network’s latencies far below the over-all 100 ms target and that due to externalities might try to get content inside their networks (and into their own data centers).
A 10-ms latency target, while much less moonshot, would be a far more economical target to strive for and might avoid substantial incremental cost of edge computing center deployments. It also resonates well with the 20 ms mentioned above, required for a great VR experience (leaving some computing and process overhead).
The 1-ms vision could be kept for use cases involving very short distances, highly ideal radio environment and with compute pretty much sitting on top of the whatever needs this performance, e.g., industrial plants, logistic / warehousing, …
Finally, the targeted extreme 5G speeds will require very substantial bandwidths. Such large bandwidths are readily available in the high frequency ranges (i.e., >3 GHz). The high frequency domain makes a lot of 5G technology challenges easier to cope with. Thus cell ranges will be (very) limited in comparison to macro cellular ones, e.g., Barclays Equity Research projects 10x times more cells will be required for 5G (10x!). 5G coverage will not match that of the macro cellular (LTE) network. In which case 5G will remain niche. With a lot less relevance to consumers. Obviously, 5G will have to jump the speed divide (a very substantial divide) to the macro cellular network to become relevant to the mass market. Little thinking appears to be spend on this challenge currently.
THE VERY FINE ART OF DETECTING MYTH & BALONEY.
Carl Sagan, in his great article The Fine Art of Baloney Detection, states that one should “Try not to get overly attached to a hypothesis just because it’s yours.”. Although Carl Sagan starts out discussing the nature of religious belief and the expectations of an afterlife, much of his “Baloney Detection Kit” applies equally well to science & technology. In particular towards our expert expectations towards consumerism and its most likely demand. After all, isn’t Technology in some respects our new modern day religion?
Some might have the impression that expectations towards 5G, is the equivalent of a belief in an afterlife or maybe more accurately resurrection of the Telco business model to its past glory. It is almost like a cosmic event, where after entropy death, the big bang gives birth to new, and supposedly unique (& exclusive) to our Telco industry, revenue streams that will make all alright (again). There clearly is some hype involved in current expectations towards 5G, although the term still has to enter the Gartner hype cycle report (maybe 2017 will be the year?).
The cynic (mea culpa) might say that it is in-evitable that there will be a 5G after 4G (that came after 3G (that came after 2G)). We also would expect 5G to be (a lot) better than 4G (that was better than 3G, etc..).
Well … Better for who? … Better for Telcos? Better for Suppliers? Better revenues? Their Shareholders? Better for our Consumers? Better for our Society? Better for (engineering) job security? … Better for Everyone and Everything? Wow! Right? … What does better mean?
Better speed … Yes! … Actually the 5G vision gives me insanely better speeds than LTE does today.
Better latency … Internal to the operator’s own network Yes! … Not per default noticeable for most consumer use cases relying on the externalities of the internet.
Better coverage … well if operators can afford to provide 100% 5G coverage then certainly Yes! Consumers would benefit even at a persistent 50 Mbps level.
Better availability …I don’t really think that Network Availability is a problem for the general consumer where there is coverage (at least not in mature markets, Myanmar absolutely … but that’s an infrastructure problem rather than a cellular standard one!) … Whether 100% availability is noticeable or not will depend a lot on the starting point.
Better (in the sense of more) revenues … Work in Progress!
Better margins … Only if incremental 5G cost to incremental 5G revenue is positive.
5G vision is flawed and not the huge advance in global connectivity as advertised.
The data rates promised by 5G will not be sufficiently valued by the users.
The envisioned 5G capacity demand will not be needed.
Most operators can simply not afford the cost required to realize 5G.
Technology advances are in-sufficient to realize the 5G vision.
Consistent connectivity is the more important aim of a 5G technology.
I recommend all to read William Webb’s well written and even better argued book. It is one for the first more official critiques of the 5G Vision. Some of the points certainly should have us pause and maybe even re-evaluate 5G priorities. If anything, it helps to sharpen 5G arguments.
Despite William Webb”s critique of 5G, one need to realize that a powerful technology vision of what 5G could be, even if very moonshot, does leapfrog innovation, needed to take a given technology too a substantially higher level, than what might otherwise be the case. If the 5G whitepaper by Rachid El Hattachi & Javan Erfanian had “just” been about better & consistent coverage, we would not have had the same technology progress independent of whether the ultimate 5G end game is completely reachable or not. Moreover, to be fair to the NGMN whitepaper, it is not that the whitepaper does not consider consistent connectivity, it very much does. It is more a matter of where lies the main attention of the industry at this moment. That attention is not on consistent connectivity but much more on niche use cases (i.e., ultra high bandwidth at ultra low latencies).
Another, very worthy 5G analysis, also from 2016, is the Barclays Equity Research “5G – A new Dawn” (September 2016) paper. The Barclays 5G analysis concludes ;
Mobile operator’s will need 10x more sites over the next 5 to 10 years driven by 5G demand.
There will be a strong demand for 5G high capacity service.
The upfront cost for 5G will be very substantial.
The cost of data capacity (i.e., Euro per GB) will fall approx. a factor 13 between LTE and 5G (note: this is “a bit” of a economic problem when capacity is supposed to increase a factor 50).
Sub-scale Telcos, including mobile-only operations, may not be able to afford 5G (note: this point, if true, should make the industry very alert towards regulatory actions).
Having a modernized super-scalable fixed broadband transport network likely to be a 5G King Maker (note: Its going to be great to be an incumbent again).
To the casual observer, it might appear that Barclays is in strong opposition to William Webb’s 5G view. However, maybe that is not completely so.
If it is true, that only very few Telco’s, primarily modernized incumbent fixed-mobile Telco’s, can afford to build 5G networks, one might argue that the 5G Vision is “somewhat” flawed economically. The root cause for this assumed economical flaw (according with Barclays, although they do not point out it is a flaw!) clearly is the very high 5G speeds, assumed to be demanded by the user. Resulting in massive increase in network densification and need for radically modernized & re-engineered transport networks to cope with this kind of demand.
Barclays assessments are fairly consistent with the illustration shown below of the likely technology cost impact, showing the challenges a 5G deployment might have;
Some of the possible operational cost improvements in IT, Platforms and Core shown in the above illustration arises from the natural evolving architectural simplifications and automation strategies expected to be in place by the time of the 5G launch. However, the expected huge increase in small cells are the root cause of most of the capital and operational cost pressures expected to arise with 5G. Depending on the original state of the telecommunications infrastructure (e.g., cloudification, virtualization,…), degree of transport modernization (e.g., fiberization), and business model (e.g., degree of digital transformation), the 5G economical impact can be relative modest (albeit momentarily painful) to brutal (i.e., little chance of financial return on investment). As discussed in the Barclays “5G – A new dawn” paper.
Furthermore, if the relative cost of delivering a 5G Byte is 13 – 14 times lower than an LTE Byte, and the 5G capacity demand is 50 times higher than LTE, the economics doesn’t work out very well. So if I can produce a 5G Byte at 1/14th of an LTE Byte, but my 5G Byte demand is 50x higher than in LTE, I could (simplistically) end up with more than 3x more absolute cost for 5G. That’s really Ugly! Although if Barclays are correct in the factor 10 higher number of 5G sites, then a (relevant) cost increase of factor 3 doesn’t seem completely unrealistic. Of course Barclays could be wrong! Unfortunately, an assessment of the incremental revenue potential has yet to be provided. If the price for a 5G Byte could be in excess of a factor 3 of an LTE Byte … all would be cool!
If there is something to be worried about, I would worry much more about the Barclays 5G analysis than the challenges of William Webb (although certainly somehow intertwined).
What is the 5G market potential in terms of connections?
Caution! Above global mobile connection forecast is likely to change many time as we approaches commercial launch and get much better impression of the 5G launch strategies of the various important players in the Telco Industry. In my own opinion, if 5G will be launched primarily in the mm-wave bands around and above 30 GHz, I would not expect to see a very aggressive 5G uptake. Possible a lot less than the above (with the danger of putting myself in the category of badly wrong forecasts of the future). If 5G would be deployed as an overlay to existing macro-cellular networks … hmmm who knows! maybe above would be a very pessimistic view of 5G uptake?
THE 5G PROMISES (WHAT OTHERS MIGHT CALL A VISION).
Let’s start with the 5G technology vision as being presented by NGMN and GSMA.
Note: ca. 5 minutes of service unavailability per year. If counted on active usage hours this would be less than 2.5 minutes per year per customer or less than 1/2 second per day per customer.
7.Perception of 100% coverage.
Note: In 2015 report from European Commission, “Broadband Coverage in Europe 2015”, for EU28, 86% of households had access to LTE overall. However, only 36% of EU28 rural households had access to LTE in 2015.
8.90% energy reduction of current network-related energy consumption.
Note: Approx. 1% of a European Mobile Operator’s total Opex.
9.Up-to 10 years battery life for low-power Internet of Things 5G devices.
The 5G whitepaper also discusses new business models and business opportunities for the Telco industry. However, there is little clarity on what would be the relevant 5G business targets. In other words, what would 5G as a technology bring, in additional Revenues, in Churn reduction, Capex & Opex (absolute) Efficiencies, etc…
More concrete and tangible economical requirements are badly required in the 5G discussion. Without it, is difficult to see how Technology can ensure that the 5G system that will be developed is also will be relevant for the business challenges in 2020 and beyond.
Today an average European Mobile operator spends approx. 40 Euro in Total Cost of Ownership (TCO) per customer per anno on network technology (and slightly less on average per connection). Assuming a capital annualization rate of 5 years and about 15% of its Opex relates to Technology (excluding personnel cost).
The 40 Euro TCO per customer per anno sustains today an average LTE EU28 customer experience of 31±9 Mbps downlink speed @ 41±9 ms (i.e., based on OpenSignal database with data as of 23 December 2016). Of course this also provides for 3G/HSPA network sustenance and what remains of the 2G network.
Thus, we might have a 5G TCO ceiling at least without additional revenue. The maximum 5G technology cost per average speed (in downlink) of 1 – 10 Gbps @ 10 ms should not be more than 40 Euro TCO per customer per anno (i.e, and preferably a lot less at the time we eventually will launch 5G in 2020).
Thus, our mantra when developing the 5G system should be:
5G should not add additional absolute cost burden to the Telecom P&L.
and also begs the question of proposing some economical requirements to partner up with the technology goals.
5G ECONOMIC REQUIREMENTS (TO BE CONSIDERED).
5G should provide new revenue opportunities in excess of 20% of access based revenue (e.g., Europe mobile access based revenue streams by 2021 expected to be in the order of 160±20 Billion Euro; thus the 5G target for Europe should be to add an opportunity of ca. 30±5 Billion in new non-access based revenues).
5G should not add to Technology TCO while delivering up-to 10 Gbps @ 10 ms (with a floor level of 1 Gbps) in urban areas.
5G focus on delivering macro-cellular customer experience at minimum 50 Mbps @ maximum 10 ms.
5G should target 20% reduction of Technology TCO while delivering up-to 10 Gbps @ 10 ms (min. 1 Gbps).
5G should keep pursuing better spectral efficiency (i.e., Mbps/MHz/cell) not only through means antennas designs, e.g., n-order MiMo and Massive-MiMo, that are largely independent of the air-interface (i.e., works as well with LTE).
Target at least 20% 5G device penetration within first 2 years of commercial launch (note: only after 20% penetration does the technology efficiency become noticeable).
In order not to increment the total technology TCO, we would at the very least need to avoid adding additional physical assets or infrastructure to the existing network infrastructure. Unless such addition provide a net removal of other physical assets and thus associated cost. This is in the current high frequency, and resulting demand for huge amount of small cells, going to be very challenging but would be less so by focusing more on macro cellular exploitation of 5G.
Thus, there need to be a goal to also overlay 5G on our existing macro-cellular network. Rather than primarily focus on small, smaller and smallest cells. Similar to what have been done for LT and was a much more challenge with UMTS (i.e., due to optimum cellular grid mismatch between the 2G voice-based and the 3G more data-centric higher frequency network).
What is the cost reference that should be kept in mind?
As shown below, the pre-5G technology cost is largely driven by access cost related to the number of deployed sites in a given network and the backhaul transmission.
Adding more sites, macro-cellular or a high number of small cells, will increase Opex and add not only a higher momentary Capex demand, but also burden future cash requirements. Unless equivalent cost can removed by the 5G addition.
Obviously, if adding additional physical assets leads to verifiable incremental margin, then accepting incremental technology cost might be perfectly okay (let”s avoid being radical financial controllers).
Though its always wise to remember;
Cost committed is a certainty, incremental revenue is not.
NAUGHTY … IMAGINE A 5G MACRO CELLULAR NETWORK (OHH JE!).
From the NGMN whitepaper, it is clear that 5G is supposed to be served everywhere (albeit at very different quality levels) and not only in dense urban areas. Given the economical constraints (considered very lightly in the NGMN whitepaper) it is obvious that 5G would be available across operators existing macro-cellular networks and thus also in the existing macro cellular spectrum regime. Not that this gets a lot of attention.
In the following, I am proposing a 5G macro cellular overlay network providing a 1 Gbps persistent connection enabled by massive MiMo antenna systems. This though experiment is somewhat at odds with the NGMN whitepaper where their 50 Mbps promise might be more appropriate. Due to the relative high frequency range in this example, massive MiMo might still be practical as a deployment option.
If you follow all the 5G news, particular on 5G trials in US and Europe, you easily could get the impression that mm-wave frequencies (e.g., 30 GHz up-to 300 GHz) are the new black.
There is the notion that;
“Extremely high frequencies means extremely fast 5G speeds”
which is baloney! It is the extremely large bandwidth, readily available in the extremely high frequency bands, that make for extremely fast 5G (and LTE of course) speeds.
We can have GHz bandwidths instead of MHz (i.e, 1,000x) to play with! … How extremely cool is that not? We totally can suck at fundamental spectral efficiency and still get out extremely high throughputs for the consumers data consumption.
While this mm-wave frequency range is very cool, from an engineering perspective and for sure academically as well, it is also extremely non-matching our existing macro-cellular infrastructure with its 700 to 2.6 GHz working frequency range. Most mobile networks in Europe have been build on a 900 or 1800 MHz fundamental grid, with fill in from UMTS 2100 MHz coverage and capacity requirements.
Being a bit of a party pooper, I asked whether it wouldn’t be cool (maybe not to the extreme … but still) to deploy 5G as an overlay on our existing (macro) cellular network? Would it not be economically more relevant to boost the customer experience across our macro-cellular networks, that actually serves our customers today? As opposed to augment the existing LTE network with ultra hot zones of extreme speeds and possible also an extreme number of small cells.
If 5G would remain an above 3 GHz technology, it would be largely irrelevant to the mass market and most use cases.
A 5G MACRO CELLULAR THOUGHT EXAMPLE.
So let’s be (a bit) naughty and assume we can free up 20MHz @ 1800 MHz. After all, mobile operators tend to have a lot of this particular spectrum anyway. They might also re-purpose 3G/LTE 2.1 GHz spectrum (possibly easier than 1800 MHz pending overall LTE demand).
In the following, I am ignoring that whatever benefits I get out of deploying higher-order MiMo or massive MiMo (mMiMo) antenna systems, will work (almost) equally well for LTE as it will for 5G (all other things being equal).
Remember we are after
A lot more speed. At least 1 Gbps sustainable user throughput (in the downlink).
Ultra-responsiveness with latencies from 10 ms and down (E2E).
No worse 5G coverage than with LTE (at same frequency).
Of course if you happen to be a NGMN whitepaper purist, you will now tell me that I my ambition should only be to provide sustainable 50 Mbps per user connection. It is nevertheless an interesting thought exercise to explore whether residential areas could be served, by the existing macro cellular network, with a much higher consistent throughput than 50 Mbps that might ultimately be covered by LTE rather than needing to go to 5G. Anywhere both Rachid El Hattachi and Jarvan Erfenian knew well enough to hedge their 5G speed vision against the reality of economics and statistical fluctuation.
and I really don’t care about the 1,000x (LTE) bandwidth per unit area promise!
Why? The 1,000x promise It is fairly trivial promise. To achieve it, I simply need a high enough frequency and a large enough bandwidth (and those two as pointed out goes nicely hand in hand). Take a 100 meter 5G-cell range versus a 1 km LTE-cell range. The 5G-cell is 100 times smaller in coverage area and with 10x more 5G spectral bandwidth than for LTE (e.g., 200 MHz 5G vs 20 MHz LTE), I would have the factor 1,000 in throughput bandwidth per unit area. This without having to assume mMiMo that I could also choose to use for LTE with pretty much same effect.
Detour to the cool world of Academia: University of Bristol published recently (March 2016) a 5G spectral efficiency of ca. 80 Mbps/MHz in a 20 MHz channel. This is about 12 times higher than state of art LTE spectral efficiency. Their base station antenna system was based on so-called massive MiMo (mMiMo) with 128 antenna elements, supporting 12 users in the cell as approx. 1.6 Gbps (i.e., 20 MHz x 80 Mbps/MHz). The proof of concept system operated 3.5 GHz and in TDD mode (note: mMiMo does not scale as well for FDD and pose in general more challenges in terms of spectral efficiency). National Instruments provides a very nice overview of 5G MMiMo systems in their whitepaper “5G Massive MiMo Testbed: From Theory to Reality”.
A picture of the antenna system is shown below;
Figure above: One of the World’s First Real-Time massive MIMO Testbeds–Created at Lund University. Source: “5G Massive MiMo (mMiMo) Testbed: From Theory to Reality” (June 2016).
For a good read and background on advanced MiMo antenna systems I recommend Chockalingam & Sundar Rajan’s book on “Large MiMo Systems” (Cambridge University Press, 2014). Though there are many excellent accounts of simple MiMo, higher-order MiMo, massive MiMo, Multi-user MiMo antenna systems and the fundamentals thereof.
Back to naughty (i.e., my 5G macro cellular network);
So let’s just assume that the above mMiMO system, for our 5G macro-cellular network,
and keeping in mind that FDD mMiMo performance tends to be lower than TDD all else being equal.
will, in due time, be available for 5G with a channel of at least 20 MHz @ 1800MHz. And at a form factor that can be integrated well with existing macro cellular design without incremental TCO.
This is a very (VERY!) big assumption. Requirements of substantially more antenna space for massive MiMo systems, at normal cellular frequency ranges, are likely to result. Structural integrity of site designs would have to be checked and possibly be re-enforced to allow for the advanced antenna system, contributing to both additional capital cost and possible incremental tower/site lease.
So we have (in theory) a 5G macro-cellular overlay network with at least cell speeds of 1+Gbps, which is ca. 10 – 20 times that of today’s LTE networks cell performance (not utilizing massive MiMo!). If I have more 5G spectrum available, the performance would increase linearly (and a bit) accordingly.
mMiMo designed for TDD, but works at some performance penalty for FDD.
mMiMo will really be deployable at low total cost of ownership (i.e., it is not enough that the antenna system itself is low cost!).
mMiMo performance leap frog comes at the price of high computational complexity (e.g., should be factored into the deployment cost).
mMiMo relies on distributed processing algorithms which at this scale is relative un-exploited territory (i.e., should be factored into the deployment cost).
But wait a minute! I might (naively) theorize away additional operational cost of the active electronics and antenna systems on the 5G cell site (overlaid on legacy already present!). I might further assume that the Capex of the 5G radio & antenna system can be financed within the regular modernization budget (assuming such a budget exists). But … But surely our access and core transport networks have not been scaled for a factor 10 – 20 (and possibly a lot more than that) in crease in throughput per active customer?
No it has not! Really Not!
Though some modernized converged Telcos might be a lot better positioned for thefixed broadband transformation required to sustain the 5G speed promise.
For most mobile operators, it is highly likely that substantial re-design and investments of transport networks will have to be made in order to support the 5G target performance increase above and beyond LTE.
Definitely a lot more on this topic in a subsequent Blog.
ON THE 5G PROMISES.
Lets briefly examine the 8 above 5G promises or visionary statements and how these impact the underlying economics. As this is an introductory chapter, the deeper dive and analysis will be referred to subsequent chapters.
NEED FOR SPEED.
PROMISE 1: From 1 to 10 Gbps in actual experienced 5G speed per connected device (at a max. of 10 ms round-trip time).
PROMISE 2: Minimum of 50 Mbps per user connection everywhere (at a max. of 10 ms round-trip time).
PROMISE 3: Thousand times more bandwidth per unit area (compared to LTE).
Before anything else, it would be appropriate to ask a couple of questions;
“Do I need this speed?” (The expert answer if you are living inside the Telecom bubble is obvious! Yes Yes Yes ….Customer will not know they need it until they have it! …).
“that kind of sustainable speed for what?” (Telekom bubble answer would be! Lots of useful things! … much better video experience, 4K, 8K, 32K –> fully emerged holographic VR experience … Lots!)
“am I willing to pay extra for this vast improvement in my experience?” (Telekom bubble answer would be … ahem … that’s really a business model question and lets just have marketing deal with that later).
What is true however is:
My objective measurable 5G customer experience, assuming the speed-coverage-reliability promise is delivered, will quantum leap to un-imaginable levels (in terms of objectively measured performance increase).
Maybe more importantly, will the 5G customer experience from the very high speed and very low latency really be noticeable to the customer? (i.e, the subjective or perceived customer experience dimension).
Let’s ponder on this!
In Europe end of 2016, the urban LTE speed and latency user experience per connection would of course depend on which network the customer would be (not all being equal);
In 2016 on average in Europe an urban LTE user, experienced a DL speed of 31±9 Mbps, UL speed of 9±2 Mbps and latency around 41±9 milliseconds. Keep in mind that OpenSignal is likely to be closer to the real user’s smartphone OTT experience, as it pings a server external to the MNOs network. It should also be noted that although the OpenSignal measure might be closer to the real customer experience, it still does not provide the full experience from for example page load or video stream initialization and start.
The 31 Mbps urban LTE user experience throughput provides for a very good video streaming experience at 1080p (e.g., full high definition video) even on a large TV screen. Even a 4K video stream (15 – 32 Mbps) might work well, provided the connection stability is good and that you have the screen to appreciate the higher resolution (i.e., a lot bigger than your 5” iPhone 7 Plus). You are unlikely to see the slightest difference on your mobile device between the 1080p (9 Mbps) and 480p (1.0 – 2.3 Mbps) unless you are healthy young and/or with a high visual acuity which is usually reserved for the healthy & young.
With 5G, the DL speed is targeted to be at least 1 Gbps and could be as high as 10 Gbps, all delivered within a round trip delay of maximum 10 milliseconds.
5G target by launch (in 2020) is to deliver at least 30+ times more real experienced bandwidth (in the DL) compared to what an average LTE user would experience in Europe 2016. The end-2-end round trip delay, or responsiveness, of 5G is aimed to be at least 4 times better than the average experienced responsiveness of LTE in 2016. The actual experience gain between LTE and 3G has been between 5 – 10 times in DL speed, approx. 3 –5 times in UL and between 2 to 3 times in latency (i.e., pinging the same server exterior to the mobile network operator).
According with Sandvine’s 2015 report on “Global Internet Phenomena Report for APAC & Europe”, in Europe approx. 46% of the downstream fixedpeak aggregate traffic comes from real-time entertainment services (e.g., video & audio streamed or buffered content such as Netflix, YouTube and IPTV in general). The same report also identifies that for Mobile (in Europe) approx. 36% of the mobile peak aggregate traffic comes from real-time entertainment. It is likely that the real share of real-time entertainment is higher, as video content embedded in social media might not be counted in the category but rather in Social Media. Particular for mobile, this would bring up the share with between 10% to 15% (more in line with what is actually measured inside mobile networks). Real-time entertainment and real-time services in general is the single most important and impacting traffic category for both fixed and mobile networks.
Video viewing experience … more throughput is maybe not better, more could be useless.
Video consumption is a very important component of real-time entertainment. It amounts to more than 90% of the bandwidth consumption in the category. The Table below provides an overview of video formats, number of pixels, and their network throughput requirements. The tabulated screen size is what is required (at a reasonable viewing distance) to detect the benefit of a given video format in comparison with the previous. So in order to really appreciate 4K UHD (ultra high definition) over 1080p FHD (full high definition), you would as a rule of thumb need double the screen size (note there are also other ways to improved the perceived viewing experience). Also for comparison, the Table below includes data for mobile devices, which obviously have a higher screen resolution in terms of pixels per inch (PPI) or dots per inch (DPI). Apart from 4K (~8 MP) and to some extend 8K (~33 MP), the 16K (~132 MP) and 32K (~528 MP) are still very yet exotic standards with limited mass market appeal (at least as of now).
We should keep in mind that there are limits to the human vision with the young and healthy having a substantial better visual acuity than what can be regarded as normal 20/20 vision. Most magazines are printed at 300 DPI and most modern smartphone displays seek to design for 300 DPI (or PPI) or more. Even Steve Jobs has addressed this topic;
However, it is fair to point out that this assumed human vision limitation is debatable (and have been debated a lot). There is little consensus on this, maybe with the exception that the ultimate limit (at a distance of 4 inch or 10 cm) is 876 DPI or approx. 300 DPI (at 11.5 inch / 30 cm).
Anyway, what really matters is the customers experience and what they perceive while using their device (e.g., smartphone, tablet, laptop, TV, etc…).
So lets do the visual acuity math for smartphone like displays;
We see (from the above chart) that for an iPhone 6/7 Plus (5.5” display) any viewing distance above approx. 50 cm, a normal eye (i.e., 20/20 vision) would become insensitive to video formats better than 480p (1 – 2.3 Mbps). In my case, my typical viewing distance is ca. 30+ cm and I might get some benefits from 720p (2.3 – 4.5 Mbps) as opposed to 480p. Sadly my sight is worse than the norm of 20/20 (i.e., old! and let’s just leave it at that!) and thus I remain insensitive to the resolution improvements 720p would provide. If you have a device with at or below 4” display (e.g., iPhone 5 & 4) the viewing distance where normal eyes become insensitive is ca. 30+ cm.
All in all, it would appear that unless cellular user equipment, and the way these are being used, changes very fundamentally the 480p to 720p range might be more than sufficient.
If this is true, it also implies that a cellular 5G user on a reliable good network connection would need no more than 4 – 5 Mbps to get an optimum viewing (and streaming) experience (i.e., 720p resolution).
The 5 Mbps streaming speed, for optimal viewing experience, is very far away from our 5G 1-Gbps promise (x200 times less)!
Assuming instead of streaming we want to download movies, assuming we lots of memory available on our device … hmmm … then a typical 480p movie could be download in ca. 10 – 20 seconds at 1Gbps, a 720p movie between 30 and 40 seconds, and a 1080p would take 40 to 50 seconds (and likely a waste due to limitations to your vision).
However with a 5G promise of super reliable ubiquitous coverage, I really should not need to download and store content locally on storage that might be pretty limited.
Downloads to cellular devices or home storage media appears somewhat archaic. But would benefit from the promised 5G speeds.
I could share my 5G-Gbps with other users in my surrounding. A typical Western European household in 2020 (i.e., about the time when 5G will launch) would have 2.17 inhabitants (2.45 in Central Eastern Europe), watching individual / different real-time content would require multiples of the bandwidth of the optimum video resolution. I could have multiple video streams running in parallel, to likely the many display devices that will be present in the consumer’s home, etc… Still even at fairly high video streaming codecs, a consumer would be far away from consuming the 1-Gbps (imagine if it was 10 Gbps!).
Okay … so video consumption, independent of mobile or fixed devices, does not seem to warrant anywhere near the 1 – 10 Gbps per connection.
Surely EU Commission wants it!
EU Member States have their specific broadband coverage objectives – namely: ‘Universal Broadband Coverage with speeds at least 30 Mbps by 2020’ (i.e, will be met by LTE!) and ‘Broadband Coverage of 50% of households with speeds at least 100 Mbps by 2020 (also likely to be met with LTE and fixed broadband means’.
The European Commission’s “Broadband Coverage in Europe 2015” reports that 49.2% of EU28 Households (HH) have access to 100 Mbps (i.e., 50.8% of all HH have access to less than 100 Mbps) or more and 68.2% to broadband speeds above 30 Mbps (i.e., 32.8% of all HH with access to less than 30 Mbps). No more than 20.9% of HH within EU28 have FTTP (e.g., DE 6.6%, UK UK 1.4%, FR 15.5%, DK 57%).
The EU28 average is pretty good and in line with the target. However, on an individual member state level, there are big differences. Also within each of the EU member states great geographic variation is observed in broadband coverage.
Interesting, the 5G promises to per user connection speed (1 – 10 Gbps), coverage (user perceived 100%) and reliability (user perceived 100%) is far more ambitious that the broadband coverage objectives of the EU member states.
So maybe indeed we could make the EU Commission and Member States happy with the 5G Throughput promise. (this point should not be underestimated).
Web browsing experience … more throughput and all will be okay myth!
So … Surely, the Gbps speeds can help provide a much faster web browsing / surfing experience than what is experienced today for LTE and for the fixed broadband? (if there ever was a real Myth!).
In other words the higher the bandwidth, the better the user’s web surfing experience should become.
While bandwidth (of course) is a factor in customers browsing experience, it is but a factor out of several that also governs the customers real & perceived internet experience; e.g., DNS Lookups (this can really mess up user experience), TCP, SSL/TLS negotiation, HTTP(S) requests, VPN, RTT/Latency, etc…
An excellent account of these various effects is given by Jim Getty’s “Traditional AQM is not enough” (i.e., AQM: Active Queue Management). Measurements (see Jim Getty’s blog) strongly indicates that at a given relative modest bandwidth (>6+ Mbps) there is no longer any noticeable difference in page load time. In my opinion there are a lot of low hanging fruits in network optimization that provides large relative improvements in customer experience than network speed alone..
Thus one might carefully conclude that, above a given throughput threshold it is unlikely that more throughput would have a significant effect on the consumers browsing experience.
More work needs to be done in order to better understand the experience threshold after which more connection bandwidth has diminishing returns on the customer’s browsing experience. However, it would appear that 1-Gbps 5G connection speed would be far above that threshold. An average web page in 2016 was 2.2 MB which from an LTE speed perspective would take 568 ms to load fully provided connection speed was the only limitation (which is not the case). For 5G the same page would download within 18 ms assuming that connection speed was the only limitation.
Downloading content (e.g., FTTP).
Now we surely are talking. If I wanted to download the whole Library of the US Congress (I like digital books!), I am surely in need for speed!?
The US Congress have estimated that the whole print collection (i.e., 26 million books) adds up to 208 terabytes.Thus assuming I have 208+ TB of storage, I could within 20+ (at 1 Gbps) to 2+ (at 20 Gbps) days download the complete library of the US Congress.
In fact, at 1 Gbps would allow me to download 15+ books per second (assuming 1 book is on average 3oo pages and formatted at 600 DPI TIFF which is equivalent to ca. 8 Mega Byte).
So clearly, for massive file sharing (music, videos, games, books, documents, etc…), the 5G speed promise is pretty cool.
Though, it does assume that consumers would continue to see a value in storing information locally on their personally devices or storage medias. The idea remains archaic, but I guess there will always be renaissance folks around.
What about 50 Mbps everywhere (at a 10 ms latency level)?
Firstly, providing a customers with a maximum latency of 10 ms with LTE is extremely challenging. It would be highly unlikely to be achieved within existing LTE networks, particular if transmission retrials are considered. From OpenSignal December 2016 measurements shown in the chart below, for urban areas across Europe, the LTE latency is on average around 41±9 milliseconds. Considering the LTE latency variation we are still 3 – 4 times away from the 5G promise. The country average would be higher than this. Clearly this is one of the reasons why the NGMN whitepaper proposes a new air-interface. As well as some heavy optimization and redesigns in general across our Telco networks.
The urban LTE persistent experience level is very reasonable but remains lower than the 5G promise of 50 Mbps, as can be seen from the chart below;
The LTE challenge however is not the customer experience level in urban areas but on average across a given geography or country. Here LTE performs substantially worse (also on throughput) than what the NGMN whitepaper’s ambition is. Let us have a look at the current LTE experience level in terms of LTE coverage and in terms of (average) speed.
Based on European Commission “Broadband Coverage in Europe 2015” we observe that on average the total LTE household coverage is pretty good on an EU28 level. However, the rural households are in general underserved with LTE. Many of the EU28 countries still lack LTE consistent coverage in rural areas. As lower frequencies (e.g., 700 – 900 MHz) becomes available and can be overlaid on the existing rural networks, often based on 900 MHz grid, LTE rural coverage can be improved greatly. This economically should be synchronized with the normal modernization cycles. However, with the current state of LTE (and rural network deployments) it might be challenging to reach a persistent level of 50 Mbps per connection everywhere. Furthermore, the maximum 10 millisecond latency target is highly unlikely to be feasible with LTE.
In my opinion, 5G would be important in order to uplift the persistent throughput experience to at least 50 Mbps everywhere (including cell edge). A target that would be very challenging to reach with LTE in the network topologies deployed in most countries (i.e., particular outside urban/dense urban areas).
The customer experience value to the general consumer of a maximum 10 millisecond latency is in my opinion difficult to assess. At a 20 ms response time would most experiences appear instantaneous. The LTE performance of ca. 40 ms E2E external server response time, should satisfy most customer experience use case requirements beside maybe VR/AR.
Nevertheless, if the 10 ms 5G latency target can be designed into the 5G standard without negative economical consequences then that might be very fine as well.
Another aspect that should be considered is the additional 5G market potential of providing a persistent 50 Mbps service (at a good enough & low variance latency). Approximately 70% of EU28 households have at least a 30 Mbps broadband speed coverage. If we look at EU28 households with at least 50 Mbps that drops to around 55% household coverage. With the 100% (perceived)coverage & reliability target of 5G as well as 50 Mbps everywhere, one might ponder the 30% to 45% potential of households that are likely underserved in term of reliable good quality broadband. Pending the economics, 5G might be able to deliver good enough service at a substantial lower cost compared more fixed centric means.
Finally, following our expose on video streaming quality, clearly a 50 Mbps persistent 5G connectivity would be more than sufficient to deliver a good viewing experience. Latency would be less of an issue in the viewing experience as longs as the variation in the latency can be kept reasonable low.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
1000 Days & a bit … As of January 2015 a Facebook share was around Double the price of their initial public offering (IPO) back in May 2012.
That increase in share price & value correspond to an investor belief (conscious or otherwise) that Facebook’s can grab around 40% of the online spend in the (near) Future versus a mediocre 20+% back in 2012.
If you had bought Facebook shares on September 4th 2012 and sold them again December 22nd 2014 (yes just before Christmas) you would have earned 5 times your original investment!
Back in 2012, some month after Facebook’s (FB) IPO, I wrote a Blog Facebook Values … Has the little boy spoken? on the FB value and how to get a feel whether the share price was Naked (“The Ugly”), Half Dressed (“The Bad”) or Nicely Dressed (“The Good”).
I asked whether it was a good time to have confidence and invest in Facebook … My answer (at the time) was that the share price of about 20 US$ (August 2012 timeframe) appeared too low compared with the potential for capturing Online Advertisement Spending Market Share, that furthermore was poised to increase substantially going forward as funds were being redirected from traditional advertisement spend to digital / online media.
The primary source of revenue for Facebook (then & now) is from Online Advertisement Spend. Thus, I looked at what long-term share (chosen arbitrarily to be 5 years) of the Online Ad Market should Facebook have in order to justify its value and share price. Very simply I ramped up the revenue share from its current value to a target share that would correspond to Facebook’s Market Capitalization or Share Price.
In the following I will ignore all the goodies that Facebook have launched or acquired over their lifetime, such as
All of the above (and much more) serves to make sure that “People use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them”. (i.e., Facebook’s vision statement) … and (I assume) to make maximum profit out of the Facebook “addiction” by providing a very efficient advertisement platform enabled by the gazillion of personal data/information & impressions we all continuously volunteer by using Facebook.
However, while the technologies (e.g., algorithms & communications software utilities) behind are very exiting it all serves one purpose … deliver the most efficient ad to the user and make as much money out of that customer touch point. I believe that the potential and value of Whatsapp is huge and in message volume already exceeds the number daily SMS transactions globally. This still largely remains un-explored by Facebook. The question will be whether FB will primarily use Whatsapp as another Ad delivery vehicle or also as a mean to generate communications revenues in both the messaging and the voice consumer segments.
The conclusion back in August 2012, was that the share price of Facebook, based on its equivalent long-term share of the the Online Ad Market Spend, appeared low and one should expect the price (and value) to increase.
Above figure: Analysis presented in my 2012 “Facebook – time to invest?”. Note that the the share price dynamics are illustrated relative to the IPO price of 38 US$ or more accurately the stock price at closing on 18th of May 2012.
So what has happened in the almost 1000 days since the Facebook IPO?
Well after the share price dropped to 17.73 (at closing) on September 4th (2012), which roughly halved the Market Cap of Facebook, the FB Journey has been one of growth.
The cynic might of course point out that so has the rest of the market. However, while for example Nasdaq100 is ca. 68% higher (as of January 20th 2015) compared to the 18-May 2012, Facebook is almost double its IPO value (over the same period). If you take the lowest point (4 Sep 2012) and the highest point (22 Dec 2014) you have a 4.6 times ratio between high & low points.
So if you did the right thing and bought the lowest and sold at the highest … well I told you so (joking!) … Congratulation would be called for!
Here is the FB Journey ( = A Walk on the Wilder Side?) seen from an investors perspective
Above figure: illustrates Facebook stock price development since the IPO until end of January 2015 with commentary to the peaks and the dips.The Red Dot on September 4th 2012 represents the lowest historical share price (i.e., 17.71 @ closing) and the Green Dot the highest historical share price (i.e., 81.45 @ closing).
Taking the stock price dynamics as shown above, how would the previous analysis come out looking at what Online Ad Revenue Share could justify (approximately) the share price development over the period.
Well … it could look something like this based on Online Advertisement Market Share;
Above Figure: illustrates the share price dynamics relative to the IPO price, i.e., 100% level (at closing 18 May 2012). Further a relative simple valuation model based on Facebook’s long-term (i.e., 5+ years) online advertisement market share is used to derive the Online Ad Spend share that a given FB share price corresponds to. The methodology has been described in detail in “A walk on the Wild Side”.
Current stock price range is fairly consistent with a 40+% long-term (i.e., 5 year linear ramp up from 2013 share and then keeping the share at the level going forward) share of the Online Advertisement Market. I would expect Facebook to hit at least 10% for 2014 (based on eMarketer data of the total online advertisement market).
Will Facebook be able to grow to 40+% share of the Online Ad Spending?
In my opinion that does not sound completely un-reasonable …
Though it would imply that (a) other social media players also relying on the Online Ad Market are going to lose their livelihood, some (b)business plans might look somewhat more sombre for others and (c)Facebook needs to take Google Head-On.
Facebook beating Q4 earnings expectations and came out at 3.851B US$ coming out at 3.84B US$ (Low) to 4.16B (US$) (high).
MAU 1,393 1,386 Million and Mobile MAU of 1,189 1,182 Million (both lower limit estimates).
Share price at closing was slightly higher than previous trading day ending at 76.24 US$ per share (+0.6%) will drop during the day of the earning call to around 71 – 72 US$ per share.
Given the already high value of the stock, I do not expect much gain over the days after the earning call. Likely to recover to the level 5 – 10 days before the Q4/Full 2014 Earning Call.
… and I might be completely mistaken (and will be crying all the way to the bank)… but at least we will know within the next hours & days to come!
… So I ended up being fairly close to MAU (0.5% lower), Mobile MAU (0.6% lower) and earnings expectations (0.3% from my lower bound). However, predicting the stock movement … yeah … not so good. Still next couple of days will be interesting to follow. Dave Wehner, Facebook Chief Financial Officer, was where careful in managing expectations for Facebook Topline in 2015. Concerns about exchange rate effects on the Topline could result in a 5% lower revenue than it would have been with 2014 exchange rates. Basically the revenue growth in US$ would reduce somewhat due to exchange rate effects. This is likely to have some negative impact on Facebook profitability and their margin as their fundamental cost base is in US$.
FB gains a sustainable share of online ad spend X%.
5 yr linear ramp-up from 2014 9.6% (assessed) to X%, and then maintained at that level.
Other revenues 15% in 2014, linearly reduced to 10% after 5 yrs and then maintained.
Assume FB can maintain a free cash flow yield of 25%.
It should be noted that the above analysis is in all likelihood oversimplifying. However it is not terrible difficult to add complexity. Though given the inherent uncertainties involved in predicting the future, the approach presented is good enough to get an idea about a given investments (or stock purchase) attractiveness.
For the financial history buffs, the Nasdaq100 is ca. 5% from the level of the dot.com foreshock (or pre-crash) of March-2000 and has surpassed the big crash of July-2000.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
Figure above: shows the share price development from May 18 2012 (IPO date of FB) to 20 January 2015 of Facebook and Nasdaq100 composite. While Facebook largely under-performed in 2012 and well into 2013, its recovery from mid-2013 until January 2015 has been spectacular.
Figure above: illustrates Laplace distribution representations of the daily returns of Facebook and Nasdaq100 over the period from 18 May 2012 to 20 January 2015. Note the slight right shift in centre point from the 0%. For a more detailed analysis of the Nasdaq100 and application of the Laplace distribution see the Business Forecasting Blog “The Nasdaq100 Daily Returns and Laplace Distributed Errors”.
Figure above: Online Advertisement Spending forecast from eMarketer (August 2013) representing the period 2013 to 2017. From 2018 and to 2022 Forecasts have been extrapolated based on 1st and 2nd derivative of the previous period growth. The resulting trend have been checked against other available projections.
Figure above: illustrates for 2013 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (source: Facebook Annual Report 2013), Region’s share of Ad Spend (source: eMarketer), Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, source: Pyramid Research), and (fixed) internet penetration (i.e., the percentage of population having access to internet).
Figure above: illustrates for 2017 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source: Authors Facebook Model), Region’s share of Ad Spend (Source: eMarketer), Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research), and (fixed) internet penetration (i.e., the percentage of population having access to internet).
Figure above: illustrates for 2013 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source: Facebook Annual Report 2013), LTE penetration (Source: Pyramid Research), WiFi residential potential estimated from the broadband household penetration (Source: Pyramid Research), and Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research). For Facebook’s Autoplay Video feature it is important for the user to either have WiFi access or for a decent cellular performance LTE.
Figure above: illustrates for 2017 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source:Source: Authors Facebook Model), LTE penetration (Source: Pyramid Research), WiFi residential potential estimated from the broadband household penetration (Source: Pyramid Research), and Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research). For Facebook’s Autoplay Video feature it is important for the user to either have WiFi access or for a decent cellular performance LTE.
Mobile data adaption can be (and usually is) very un-healthy for the mobile voice revenues.
A Mega Byte of Mobile Voice is 6 times more expensive than a Mega Byte of Mobile Data (i.e., global average)
If customers would pay the Mobile Data Price for Mobile Voice, 50% of Global Mobile Revenue would Evaporate (based on 2013 data).
Classical Mobile Voice is not Dead! Global Mobile Voice Usage grew with more than 50% over the last 5 years. Though Global Voice Revenue remained largely constant (over 2009 – 2013).
Mobile Voice Revenues declined in most Western European & Central Eastern European countries.
Voice Revenue in Emerging Mobile-Data Markets (i.e., Latin America, Africa and APAC) showed positive growth although decelerating.
Mobile Applications providing high-quality (often High Definition) mobile Voice over IP should be expected to dent the classical mobile voice revenues (as Apps have impacted SMS usage & revenue).
Most Western & Central Eastern European markets shows an increasing decline in price elasticity of mobile voice demand. Even some markets (regions) had their voice demand decline as the voice prices were reduced (note: not that causality should be deduced from this trend though).
The Art of Re-balancing (or re-capture) the mobile voice revenue in data-centric price plans are non-trivial and prone to trial-and-error (but likely also un-avoidable).
An Unbearable Lightness.
There is something almost perverse about how light the mobile industry tends to treat Mobile Voice, an unbearable lightness?
How often don’t we hear Telco Executives wish for All-IP and web-centric services for All? More and more mobile data-centric plans are being offered with voice as an after thought. Even though voice still constitute more than 60% of the Global Mobile turnover and in many emerging mobile markets beyond that. Even though classical mobile voice is more profitable than true mobile broadband access. “Has the train left the station” for Voice and running off the track? In my opinion, it might have for some Telecom Operators, but surely not for all. Taking some time away from thinking about mobile data would already be an incredible improvement if spend on strategizing and safeguarding mobile voice revenues that still are a very substantial part of The Mobile Business Model.
Mobile data penetration is un-healthy for voice revenue. It is almost guarantied that voice revenue will start declining as the mobile data penetration reaches 20% and beyond. There are very few exceptions (i.e., Australia, Singapore, Hong Kong and Saudi Arabia) to this rule as observed in the figure below. Much of this can be explained by the Telecoms focus on mobile data and mobile data centric strategies that takes the mobile voice business for given or an afterthought … focusing on a future of All-IP Services where voice is “just” another data service. Given the importance of voice revenues to the mobile business model, treating voice as an afterthought is maybe not the most value-driven strategy to adopt.
I should maybe point out that this is not per se a result of the underlying Cellular All-IP Technology. The fact is that Cellular Voice over an All-IP network is very well specified within 3GPP. Voice over LTE (i.e., VoLTE), or Voice over HSPA (VoHSPA) for that matter, is enabled with the IP Multimedia Subsystem (IMS). Both VoLTE and VoHSPA, or simply Cellular Voice over IP (Cellular VoIP as specified by 3GPP), are highly spectral efficient (compared to their circuit switched equivalents). Further the Cellular VoIP can be delivered at a high quality comparable to or better than High Definition (HD) circuit switched voice. Recent Mean Opinion Score (MOS) measurements by Ericsson and more recently (August 2014) Signals Research Group & Spirent have together done very extensive VoLTE network benchmark tests including VoLTE comparison with the voice quality of 2G & 3G Voice as well as Skype (“Behind the VoLTE Curtain, Part 1. Quantifying the Performance of a Commercial VoLTE Deployment”). Further advantage of Cellular VoIP is that it is specified to inter-operate with legacy circuit-switched networks via the circuit-switched fallback functionality. An excellent account for Cellular VoIP and VoLTE in particular can be found in Miikki Poikselka et al’s great book on “Voice over LTE” (Wiley, 2012).
Its not the All-IP Technology that is wrong, its the commercial & strategic thinking of Voice in an All-IP World that leaves a lot to be wished for.
Voice over LTE provides for much better Voice Quality than a non-operator controlled (i.e., OTT) mobile VoIP Application would be able to offer. But is that Quality worth 5 to 6 times the price of data, that is the Billion $ Question.
Figure Above: illustrates the compound annual growth rates (2009 to 2013) of mobile voice revenue and the mobile data penetration at the beginning of the period (i.e., 2009). As will be addressed later it should be noted that the growth of mobile voice revenues are NOT only depending on Mobile Data Penetration Rates but on a few other important factors, such as addition of new unique subscribers, the minute price and the voice arpu compared to the income level (to name a few). Analysis has been based on Pyramid Research data. Abbreviations: WEU: Western Europe, CEE: Central Eastern Europe, APAC: Asia Pacific, MEA: Middle East & Africa, NA: North America and LA: Latin America.
In the following discussion classical mobile voice should be understood as an operator-controlled voice service charged by the minute or in equivalent economical terms (i.e., re-balanced data pricing). This is opposed to a mobile-application-based voice service (outside the direct control of the Telecom Operator) charged by the tariff structure of a mobile data package without imposed re-balancing.
If the Industry would charge a Mobile Voice Minute the equivalent of what they charge a Mobile Mega Byte … almost 50% of Mobile Turnover would disappear … So be careful AND be prepared for what you wish for!
There are at least a couple of good reasons why Mobile Operators should be very focused on preserving mobile voice as we know it (or approximately so) also in LTE (and any future standards). Even more so, Mobile Operators should try to avoid too many associations with non-operator controlled Voice-over-IP (VoIP) Smartphone applications (easier said than done .. I know). It will be very important to define a future voice service on the All-IP Mobile Network that maintains its economics (i.e., pricing & margin) and don’t get “confused” with the mobile-data-based economics with substantially lower unit prices & questionable profitability.
Back in 2011 at the Mobile Open Summit, I presented “Who pays for Mobile Broadband” (i.e., both in London & San Francisco) with the following picture drawing attention to some of the Legacy Service (e.g., voice & SMS) challenges our Industry would be facing in the years to come from the many mobile applications developed and in development;
One of the questions back in 2011 was (and Wow it still is! …) how to maintain the Mobile ARPU & Revenues at a reasonable level, as opposed to massive loss of revenue and business model sustainability that the mobile data business model appeared to promise (and pretty much still does). Particular the threat (& opportunities) from mobile Smartphone applications. Mobile Apps that provides Mobile Customers with attractive price-arbitrage compared to their legacy prices for SMS and Classical Voice.
“IP killed the SMS Star” … Will IP also do away with the Classical Mobile Voice Economics as well?
Okay … Lets just be clear about what is killing SMS (it’s hardly dead yet). The Mobile Smartphone Messaging-over-IP (MoIP) App does the killing. However, the tariff structure of an SMS vis-a-vis that of a mobile Mega Byte (i..e, ca. 3,000x) is the real instigator of the deed together with the shear convenience of the mobile application itself.
As of August 2014 the top Messaging & Voice over IP Smartphone applications share ca. 2.0+ Billion Active Users (not counting Facebook Messenger and of course with overlap, i.e., active users having several apps on their device). WhatsApp is the Number One Mobile Communications App with about 700 Million active users (i.e., up from 600 Million active users in August 2014). Other Smartphone Apps are further away from the WhatsApp adaption figures. Applications from Viber can boast of 200+M active users, WeChat (predominantly popular in Asia) reportedly have 460+M active users and good old Skype around 300+M active users. The impact of smartphone MoIP applications on classical messaging (e.g., SMS) is well evidenced. So far Mobile Voice-over-IP has not visible dented the Telecom Industry’s mobile voice revenues. However the historical evidence is obviously no guaranty that it will not become an issue in the future (near, medium or far).
WhatsApp is rumoured to launch mobile voice calling as of first Quarter of 2015 … Will this event be the undoing of operator controlled classical mobile voice? WhatsApp already has taken the SMS Scalp with 30 Billion WhatsApp messages send per day according the latest data from WhatsApp (January 2015). For comparison the amount of SMS send out over mobile networks globally was a bit more than 20 Billion per day (source: Pyramid Research data). It will be very interesting (and likely scary as well) to follow how WhatsApp Voice (over IP) service will impact Telecom operator’s mobile voice usage and of course their voice revenues. The Industry appears to take the news lightly and supposedly are unconcerned about the prospects of WhatsApp launching a mobile voice services (see: “WhatsApp voice calling – nightmare for mobile operators?” from 7 January 2015) … My favourite lightness is Vodacom’s (South Africa) “if anything, this vindicates the massive investments that we’ve been making in our network….” … Talking about unbearable lightness of mobile voice … (i.e., 68% of the mobile internet users in South Africa has WhatsApp on their smartphone).
Paying the price of a mega byte mobile voice.
A Mega-Byte is not just a Mega-Byte … it is much more than that!
In 2013, the going Global average rate of a Mobile (Data) Mega Byte was approximately 5 US-Dollar Cent (or a Nickel). A Mega Byte (MB) of circuit switched voice (i.e., ca. 11 Minutes @ 12.2kbps codec) would cost you 30+ US$-cent or about 6 times that of Mobile Data MB. Would you try to send a MB of SMS (i.e., ca. 7,143 of them) that would cost you roughly 150 US$ (NOTE: US$ not US$-Cents).
1 Mobile MB = 5 US$-cent Data MB < 30+ US$-cent Voice MB (6x mobile data) << 150 US$ SMS MB (3000x mobile data).
A Mega Byte of voice conversation is pretty un-ambiguous in the sense of being 11 minutes of a voice conversation (typically a dialogue, but could be monologue as well, e.g., voice mail or an angry better half) at a 12.2 kbps speech codec. How much mega byte a given voice conversation will translate into will depend on the underlying speech coding & decoding (codec) information rate, which typically is 12.2 kbps or 5.9 kbps (i.e., for 3GPP cellular-based voice). In general we would not be directly conscious about speed (e.g., 12.2 kbps) at which our conversation is being coded and decoded although we certainly would be aware of the quality of the codec itself and its ability to correct errors that will occur in-between the two terminals. For a voice conversation itself, the parties that engage in the conversation is pretty much determining the duration of the conversation.
An SMS is pretty straightforward and well defined as well, i.e., being 140 Bytes (or characters). Again the underlying delivery speed is less important as for most purposes it feels that the SMS sending & delivery is almost instantaneously (though the reply might not be).
All good … but what about a Mobile Data Byte? As a concept it could by anything or nothing. A Mega Byte of Data is Extremely Ambiguous. Certainly we get pretty upset if we perceive a mobile data connection to be slow. But the content, represented by the Byte, would obviously impact our perception of time and whether we are getting what we believe we are paying for. We are no longer master of time. The Technology has taken over time.
Some examples: A Mega Byte of Voice is 11 minutes of conversation (@ 12.2 kbps). A Mega Byte of Text might take a second to download (@ 1 Mbps) but 8 hours to process (i.e., read). A Mega Byte of SMS might be delivered (individually & hopefully for you and your sanity spread out over time) almost instantaneously and would take almost 16 hours to read through (assuming English language and an average mature reader). A Mega Byte of graphic content (e.g., a picture) might take a second to download and milliseconds to process. Is a Mega Byte (MB) of streaming music that last for 11 seconds (@ 96 kbps) of similar value to a MB of Voice conversation that last for 11 minutes or a MB millisecond picture (that took a second to download).
In my opinion the answer should be clearly NO … Such (somewhat silly) comparisons serves to show the problem with pricing and valuing a Mega Byte. It also illustrates the danger of ambiguity of mobile data and why an operator should try to avoid bundling everything under the banner of mobile data (or at the very least be smart about it … whatever that means).
I am being a bit naughty in above comparisons, as I am freely mixing up the time scales of delivering a Byte and the time scales of neurological processing that Byte (mea culpa).
Figure Above: Logarithmic representation of the cost per Mega Byte of a given mobile service. 1 MB of Voice is roughly corresponding to 11 Minutes at a 12.2 voice codec which is ca. 25+ times the monthly global MoU usage. 1 MB of SMS correspond to ca. 7,143 SMSs which is a lot (actually really a lot). In USA 7,143 would roughly correspond to a full years consumption. However, in WEU 7,143 SMS would be ca. 6+ years of SMS consumption (on average) to about almost 12 years of SMS consumption in MEA Region. Still SMS remain proportionate costly and clear is an obvious service to be rapidly replaced by mobile data as it becomes readily available. Source: Pyramid Research.
The “Black” Art of Re-balancing … Making the Lightness more Bearable?
I recently had a discussion with a very good friend (from an emerging market) about how to recover lost mobile voice revenues in the mobile data plans (i.e., the art of re-balancing or re-capturing). Could we do without Voice Plans? Should we focus on All-in the Data Package? Obviously, if you would charge 30+ US$-cent per Mega Byte Voice, while you charge 5 US$-cent for Mobile Data, that might not go down well with your customers (or consumer interest groups). We all know that “window-dressing” and sleight-of-hand are important principles in presenting attractive pricings. So instead of Mega Byte voice we might charge per Kilo Byte (lower numeric price), i.e., 0.029 US$-cent per kilo byte (note: 1 kilo-byte is ca. 0.65 seconds @ 12.2 kbps codec). But in general the consumer are smarter than that. Probably the best is to maintain a per time-unit charge or to Blend in the voice usage & pricing into the Mega Byte Data Price Plan (and hope you have done your math right).
Example (a very simple one): Say you have 500 MB mobile data price plan at 5 US$-cent per MB (i.e., 25 US$). You also have a 300 Minute Mobile Voice Plan of 2.7 US$-cent a minute (or 30 US$-cent per MB). Now 300 Minutes corresponds roughly to 30 MB of Voice Usage and would be charged ca. 9$. Instead of having a Data & Voice Plan, one might have only the Data Plan charging (500 MB x 5 US$cent/MB + 30 MB x 30 US$/cent/MB) / 530 MB or 6.4 US$-cent per MB (or 1.4 US$-cent more for mobile voice over the data plan or a 30% surcharge for Voice on the Mobile Data Bytes). Obviously such a pricing strategy (while simple) does pose some price strategic challenges and certainly does not per se completely safeguard voice revenue erosion. Keeping Mobile Voice separately from Mobile Data (i.e., Minutes vs Mega Bytes) in my opinion will remain the better strategy. Although such a minutes-based strategy is easily disrupted by innovative VoIP applications and data-only entrepreneurs (as well as Regulator Authorities).
Re-balancing (or re-capture) the voice revenue in data-centric price plans are non-trivial and prone to trial-and-error. Nevertheless it is clearly an important pricing strategy area to focus on in order to defend existing mobile voice revenues from evaporating or devaluing by the mobile data price plan association.
Is Voice-based communication for the Masses (as opposed to SME, SOHO, B2B,Niche demand, …) technologically un-interesting? As a techno-economist I would say far from it. From the GSM to HSPA and towards LTE, we have observed a quantum leap, a factor 10, in voice spectral efficiency (or capacity), substantial boost in link-budget (i.e., approximately 30% more geographical area can be covered with UMTS as opposed to GSM in apples for apples configurations) and of course increased quality (i.e., high-definition or crystal clear mobile voice). The below Figure illustrates the progress in voice capacity as a function of mobile technology. The relative voice spectral efficiency data in the below figure has been derived from one of the best (imo) textbooks on mobile voice “Voice over LTE” by Miikki Poikselka et all (Wiley, 2012);
Figure Above: Abbreviation guide; EFR: Enhanced Full Rate, AMR: Adaptive Multi-Rate, DFCA: Dynamic Frequency & Channel Allocation, IC: Interference Cancellation. What might not always be appreciate is the possibility of defining voice over HSPA, similar to Voice over LTE. Source: “Voice over LTE” by Miikki Poikselka et all (Wiley, 2012).
If you do a Google Search on Mobile Voice you would get ca. 500 Million results (note Voice over IP only yields 100+ million results). Try that on Mobile Data and “sham bam thank you mam” you get 2+ Billion results (and projected to increase further). For most of us working in the Telecom industry we spend very little time on voice issues and an over-proportionate amount of time on broadband data. When you tell your Marketing Department that a state-of-the-art 3G can carry at least twice as much voice traffic than state-of-the –art GSM (and over 30% more coverage area) they don’t really seem to get terribly exited? Voice is un-sexy!? an afterthought!? … (don’t even go brave and tell Marketing about Voice over LTE, aka VoLTE).
Is Mobile Voice Dead or at the very least Dying?
Is Voice un-interesting, something to be taken for granted?
Is Voice “just” data and should be regarded as an add-on to Mobile Data Services and Propositions?
From a Mobile Revenue perspective mobile voice is certainly not something to be taken for granted or just an afterthought. In 2013, mobile voice still amounted for 60+% of he total global mobile turnover, with mobile data taking up ca. 40% and SMS ca. 10%. There are a lot of evidence that SMS is dying out quickly with the emergence of smartphones and Messaging-over-IP-based mobile application (SMS – Assimilation is inevitable, Resistance is Futile!). Not particular surprising given the pricing of SMS and the many very attractive IP-based alternatives. So are there similar evidences of mobile voice dying?
NO! NIET! NEM! MA HO BU! NEJ!(not any time soon at least)
Lets see what the data have to say about mobile voice?
In the following I only provide a Regional but should there be interest I have very detailed deep dives for most major countries in the various regions. In general there are bigger variations to the regional averages in Middle East & Africa (i.e., MEA) as well as Asia Pacific (i.e., APAC) Regions, as there is a larger mix of mature and emerging markets with fairly large differences in mobile penetration rates and mobile data adaptation in general. Western Europe, Central Eastern Europe, North America (i.e., USA & Canada) and Latin America are more uniform in conclusions that can reasonably be inferred from the averages.
As shown in the Figure below, from 2009 to 2013, the total amount of mobile minutes generated globally increased with 50+%. Most of that increase came from emerging markets as more share of the population (in terms of individual subscribers rather than subscriptions) adapted mobile telephony. In absolute terms, the global mobile voice revenues did show evidence of stagnation and trending towards decline.
Figure Above: Illustrates the development & composition of historical Global Mobile Revenues over the period 2009 to 2013. In addition also shows the total estimated growth of mobile voice minutes (i.e., Red Solid Curve showing MoUs in units of Trillions) over the period. Sources: Pyramid Research & Statista. It should noted that various data sources actual numbers (over the period) are note completely matching. I have observed a difference between various sources of up-to 15% in actual global values. While interesting this difference does not alter the analysis & conclusions presented here.
If all voice minutes was charged with the current Rate of Mobile Data, approximately Half-a-Billion US$ would evaporate from the Global Mobile Revenues.
So while mobile voice revenues might not be a positive growth story its still “sort-of” important to the mobile industry business.
Most countries in Western & Central Eastern Europe as well as mature markets in Middle East and Asia Pacific shows mobile voice revenue decline (in absolute terms and in their local currencies). For Latin America, Africa and Emerging Mobile Data Markets in Asia-Pacific almost all exhibits positive mobile voice revenue growth (although most have decelerating growth rates).
Figure Above: Illustrates the annual growth rates (compounded) of total mobile voice revenues and the corresponding growth in mobile voice traffic (i.e., associated with the revenues). Some care should be taken as for each region US$ has been used as a common currency. In general each individual country within a region has been analysed based on its own local currency in order to avoid mixing up currency exchange effects. Source: Pyramid Research.
Of course revenue growth of the voice service will depend on (1) the growth of subscriber base, (2) the growth of the unit itself (i.e., minutes of voice usage) as it is used by the subscribers (i.e., which is likely influenced by the unit price), and (3) the development of the average voice revenue per subscriber (or user) or the unit price of the voice service. Whether positive or negative growth of Revenue results, pretty much depends on the competitive environment, regulatory environment and how smart the business is in developing its pricing strategy & customer acquisition & churn dynamics.
Growth of (unique) mobile customers obviously depends the level of penetration, network coverage & customer affordability. Growth in highly penetrated markets is in general (much) lower than growth in less mature markets.
Figure Above: Illustrates the annual growth rates (compounded) of unique subscribers added to a given market (or region). Further to illustrate the possible relationship between increased subscribers and increased total generated mobile minutes the previous total minutes annual growth is shown as well. Source: Pyramid Research.
Interestingly, particular for the North America Region (NA), we see an increase in unique subscribers of 11% per anno and hardly any growth over the period of total voice minutes. Firstly, note that the US Market will dominate the averaging of the North America Region (i.e., USA and Canada) having approx. 13 times more subscribers. So one of the reasons for this no-minutes-growth effect is that the US market saw a substantial increase in the prepaid ratio (i.e., from ca.19% in 2009 to 28% in 2013). Not only were new (unique) prepaid customers being added. Also a fairly large postpaid to prepaid migration took place over the period. In the USA the minute usage of a prepaid is ca. 35+% lower than that of a postpaid. In comparison the Global demanded minutes difference is 2.2+ times lower prepaid minute usage compared to that of a postpaid subscriber). In the NA Region (and of course likewise in the USA Market) we observe a reduced voice usage over the period both for the postpaid & prepaid segment (based on unique subscribers). Thus increased prepaid blend in the overall mobile base with a relative lower voice usage combined with a general decline in voice usage leads to a pretty much zero growth in voice usage in the NA Market. Although the NA Region is dominated by USA growth (ca. 0.1 % CAGR total voice growth), Canada’s likewise showed very minor growth in their overall voice usage as well (ca. 3.8% CAGR). Both Canada & USA reduced their minute pricing over the period.
Note on US Voice Usage & Revenues: note that in both in US and in Canada also the receiving party pays (RPP) for receiving a voice call. Thus revenue generating minutes arises from both outgoing and incoming minutes. This is different from most other markets where the Calling Party Pays (CPP) and only minutes originating are counted in the revenue generation. For example in USA the Minutes of Use per blended customer was ca. 620 MoU in 2013. To make that number comparable with say Europe’s 180 MoU, one would need to half the US figure to 310 MoU still a lot higher than the Western European blended minutes of use. The US bundles are huge (in terms of allowed minutes) and likewise the charges outside bundles (i.e., forcing the consumer into the next one) though the fixed fees tends be high to very high (in comparison with other mobile markets). The traditional US voice plan would offer unlimited on-net usage (i.e., both calling & receiving party are subscribing to the same mobile network operator) as well as unlimited off-peak usage (i.e., evening/night/weekends). It should be noted that many new US-based mobile price plans offers data bundles with unlimited voice (i.e., data-centric price plans). In 2013 approximately 60% of the US mobile industry’s turnover could be attributed to mobile voice usage. This number is likely somewhat higher as some data-tariffs has voice-usage (e.g., typically unlimited) embedded. In particular the US mobile voice business model would be depending customer migration to prepaid or lower-cost bundles as well as how well the voice-usage is being re-balanced (and re-captured) in the Data-centric price plans.
The second main component of the voice revenue is the unit price of a voice minute. Apart from the NA Region, all markets show substantial reductions in the unit price of a minute.
Figure Above: Illustrating the annual growth (compounded) of the per minute price in US$-cents as well as the corresponding growth in total voice minutes. The most affected by declining growth is Western Europe & Central Eastern Europe although other more-emerging markets are observed to have decelerating voice revenue growth. Source: Pyramid Research.
Clearly from the above it appears that the voice “elastic” have broken down in most mature markets with diminishing (or no return) on further minute price reductions. Another way of looking at the loss (or lack) of voice elasticity is to look at the unit-price development of a voice-minute versus the growth of the total voice revenues;
Figure Above: Illustrates the growth of Total Voice Revenue and the unit-price development of a mobile voice minute. Apart from the Latin America (LA) and Asia Pacific (APAC) markets there clearly is no much further point in reducing the price of voice. Obviously, there are other sources & causes, than the pure gain of elasticity, effecting the price development of a mobile voice minute (i.e., regulatory, competition, reduced demand/voice substitution, etc..). Note US$ has been used as the unifying currency across the various markets. Despite currency effects the trend is consistent across the markets shown above. Source:Pyramid Research.
While Western & Central-Eastern Europe (WEU & CEE) as well as the mature markets in Middle East and Asia-Pacific shows little economic gain in lowering voice price, in the more emerging markets (LA and Africa) there are still net voice revenue gains to be made by lowering the unit price of a minute (although the gains are diminishing rapidly). Although most of the voice growth in the emerging markets comes from adding new customers rather than from growth in the demand per customer itself.
Figure Above: Illustrating possible drivers for mobile voice growth (positive as well as negative); such as Mobile Data Penetration 2013 (expected negative growth impact), increased number of (unique) subscribers compared to 2009 (expected positive growth impact) and changes in prepaid-postpaid blend (a negative %tage means postpaid increased their proportion while a positive %tage translates into a higher proportion of prepaid compared to 2009). Voice tariff changes have been observed to have elastic effects on usage as well although the impact changes from market to market pending on maturity. Source: derived from Pyramid Research.
With all the talk about Mobile Data, it might come as a surprise that Voice Usage is actually growing across all regions with the exception of North America. The sources of the Mobile Voice Minutes Growth are largely coming from
Adding new unique subscribers (i.e., increasing mobile penetration rates).
Transitioning existing subscribers from prepaid to postpaid subscriptions (i.e., postpaid tends to have (a lot) higher voice usage compared to prepaid).
General increase in usage per individual subscriber (i.e., few markets where this is actually observed irrespective of the general decline in the unit cost of a voice minute).
To the last point (#3) it should be noted that the general trend across almost all markets is that Minutes of Use per Unique customer is stagnating and even in decline despite substantial per unit price reduction of a consumed minute. In some markets that trend is somewhat compensated by increase of postpaid penetration rates (i.e., postpaid subscribers tend to consume more voice minutes). The reduction of MoUs per individual subscriber is more significant than a subscription-based analysis would let on.
Clearly, Mobile Voice Usage is far from Dead
Mobile Voice Revenue is a very important part of the overall mobile revenue composition.
It might make very good sense to spend a bit more time on strategizing voice, than appears to be the case today. If mobile voice remains just an afterthought of mobile data, the Telecom industry will loose massive amounts of Revenues and last but not least Profitability.
Post Script: What drives the voice minute growth?
An interesting exercise is to take all the data and run some statistical analysis on it to see what comes out in terms of main drivers for voice minute growth, positive as well as negative. The data available to me comprises 77 countries from WEU (16), CEE (8), APAC (15), MEA (17), NA (Canada & USA) and LA (19). I am furthermore working with 18 different growth parameters (e.g., mobile penetration, prepaid share of base, data adaptation, data penetration begin of period, minutes of use, voice arpu, voice minute price, total minute volume, customers, total revenue growth, sms, sms price, pricing & arpu relative to nominal gdp etc…) and 7 dummy parameters (populated with noise and unrelated data).
Two specific voice minute growth models emerges our of a comprehensive analysis of the above described data. The first model is as follows
(1) Voice Growth correlates positively with Mobile Penetration (of unique customers) in the sense of higher penetration results in more minutes, it correlates negatively with Mobile Data Penetration at the begin of the period (i.e., 2009 uptake of 3G, LTE and beyond) in the sense that higher mobile data uptake at the begin of the period leads to a reduction of Voice Growth, and finally Voice Growth correlates negatively with the Price of a Voice Minute in the sense of higher prices leads to lower growth and lower prices leads to higher growth. This model is statistically fairly robust (e.g., a p-values < 0.0001) as well as having all parameters with a statistically meaningful confidence intervals (i.e., upper & lower 95% confidence interval having the same sign).
The Global Analysis does pin point to very rational drivers for mobile voice usage growth, i.e., that mobile penetration growth, mobile data uptake and price of a voice minute are important drivers for total voice usage.
It should be noted that changes in the prepaid proportion does not appear statistically to impact voice minute growth.
The second model provides a marginal better overall fit to the Global Data but yields slightly worse p-values for the individual descriptive parameters.
(2) The second model simply adds the Voice ARPU to (nominal) GDP ratio to the first model. This yields a negative correlation in the sense that a low ratio results in higher voice usage growth and a higher ration in lower voice usage growth.
Both models describe the trends or voice growth dynamics reasonably well, although less convincing for Western & Central Eastern Europe and other more mature markets where the model tends to overshoot the actual data. One of the reasons for this is that the initial attempt was to describe the global voice growth behaviour across very diverse markets.
Figure Above: Illustrates total annual generated voice minutes compound annual growth rate (between 2009 and 2013) for 77 markets across 6 major regions (i.e., WEU, CEE, APAC, MEA, NA and LA). The Model 1 shows an attempt to describe the Global growth trend across all 77 markets within the same model. The Global Model is not great for Western Europe and part of the CEE although it tends to describe the trends between the markets reasonably.
Figure Western & Central Eastern Region: the above Illustrates the compound annual growth rate (2009 – 2013) of total generated voice minutes and corresponding voice revenues. For Western & Central Eastern Europe while the generated minutes have increased the voice revenue have consistently declined. The average CAGR of new unique customers over the period was 1.2% with the maximum being little less than 4%.
Figure Asia Pacific Region: the above Illustrates the compound annual growth rate (2009 – 2013) of total generated voice minutes and corresponding voice revenues. For the Emerging market in the region there is still positive growth of both minutes generated as well as voice revenue generated. Most of the mature markets the voice revenue growth is negative as have been observed for mature Western & Central Eastern Europe.
Figure Middle East & Africa Region: the above Illustrates the compound annual growth rate (2009 – 2013) of total generated voice minutes and corresponding voice revenues. For the Emerging market in the region there is still positive growth of both minutes generated as well as voice revenue generated. Most of the mature markets the voice revenue growth is negative as have been observed for mature Western & Central Eastern Europe.
Figure North & Latin America Region: the above Illustrates the compound annual growth rate (2009 – 2013) of total generated voice minutes and corresponding voice revenues. For the Emerging market in the region there is still positive growth of both minutes generated as well as voice revenue generated. Most of the mature markets the voice revenue growth is negative as have been observed for mature Western & Central Eastern Europe.
PS.PS. Voice Tariff Structure
Typically the structure of a mobile voice tariff (or how the customer is billed) is structure as follows
Fixed charge / fee
This fixed charge can be regarded as an access charge and usually is associated with a given usage limit (i.e., $ X for Y units of usage) or bundle structure.
Variable per unit usage charge
On-net – call originating and terminating within same network.
Off-net – Domestic Mobile.
Off-net – Domestic Fixed.
Off-net – International.
Local vs Long-distance.
Peak vs Off-peak rates (e.g., off-peak typically evening/night/weekend).
Roaming rates (i.e., when customer usage occurs in foreign network).
Special number tariffs (i.e., calls to paid-service numbers).
How a fixed vis-a-vis variable charges are implemented will depend on the particularity of a given market but in general will depend on service penetration and local vs long-distance charges.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. I certainly have not always been very present during the analysis and writing. Also many thanks to Shivendra Nautiyal and others for discussing and challenging the importance of mobile voice versus mobile data and how practically to mitigate VoIP cannibalization of the Classical Mobile Voice.
A Mature & Emerging Market Profitability Analysis … From Past, through Present & to the Future.
I dedicate this Blog to David Haszeldine whom has been (and will remain) a true partner when it comes to discussing, thinking and challenging cost structures, corporate excesses and optimizing the Telco profitability.
Opex growth & declining revenue growth is the biggest exposure to margin decline & profitability risk for emerging growth markets as well as mature mobile markets.
48 Major Mobile Market’s Revenue & Opex Growth have been analyzed over the period 2007 to 2013 (for some countries from 2003 to 2013). The results have been provided in an easy to compare overview chart.
For 23 out of the 48 Mobile Markets, Opex have grown faster than Revenue and poses a substantial risk to Telco profitability in the near & long-term unless Opex will be better managed and controlled.
Mobile Profitability Risk is a substantial Emerging Growth Market Problem where cost has grown much faster than the corresponding Revenues.
11 Major Emerging Growth Markets have had an Opex compounded annual growth rate between 2007 to 2013 that was higher than the Revenue Growth substantially squeezing margin and straining EBITDA.
On average the compounded annual growth rate of Opex grew 2.2% faster than corresponding Revenue over the period 2007 to 2013. Between 2012 to 2013 Opex grew (on average) 3.7% faster than Revenue.
A Market Profit Sustainability Risk Index (based on Bayesian inference) is proposed as a way to provide an overview of mobile markets profitability directions based on their Revenue and Opex growth rates.
Statistical Analysis on available data shows that a Mobile Markets Opex level is driven by (1) Population, (2) Customers, (3) Penetration and (4) ARPU. The GDP & Surface Area have only minor and indirect influence on the various markets Opex levels.
A profitability framework for understanding individual operators profit dynamics is proposed.
It is shown that Profitability can be written as withbeing the margin, with ou and ru being the user dependent OpEx and Revenue (i.e., AOPU and ARPU), of the fixed OpEx divided by the Total Subscriber Market andis the subscriber market share.
The proposed operator profitability framework provides a high degree of descriptive power and understanding of individual operators margin dynamics as a function of subscriber market share as well as other important economical drivers.
I have long & frequently been pondering over the mobile industry’s profitability.In particular, I have spend a lot of my time researching the structure & dynamics of profitability and mapping out factors that contributes in both negative & positive ways? My interest is the underlying cost structures and business models that drives the profitability in both good and bad ways. I have met Executives who felt a similar passion for strategizing, optimizing and managing their companies Telco cost structures and thereby profit and I have also met Executives who mainly cared for the Revenue.
Obviously, both Revenue and Cost are important to optimize. This said it is wise to keep in mind the following Cost- structure & Revenue Heuristics;
Cost is an almost Certainty once made & Revenues are by nature Uncertain.
Cost left Unmanaged will by default Increase over time.
Revenue is more likely to Decrease over time than increase.
Majority of Cost exist on a different & longer time-scale than Revenue.
In the following I will use EBITDA, which stands for Earnings Before Interest, Taxes, Depreciation and Amortization, as a measure of profitability and EBITDA to Revenue Ratio as a measure of my profit margin or just margin. It should be clear that EBITDA is a proxy of profitability and as such have shortfalls in specific Accounting and P&L Scenarios. Also according with GAAP (General Accepted Accounting Principles) and under IFRS (International Financial Reporting Standards) EBITDA is not a standardized accepted accounting measure. Nevertheless, both EBITDA and EBITDA Margin are widely accepted and used in the mobile industry as a proxy for operational performance and profitability. I am going to assume that for most purposes & examples discussed in this Blog, EBITDA & the corresponding Margin remains sufficiently good measures profitability.
While I am touching upon mobile revenues as an issue for profitability, I am not going to provide much thoughts on how to boost revenues or add new incremental revenues that might compensate from loss of mobile legacy service revenues (i.e., voice, messaging and access). My revenue focus in particular addresses revenue growth on a more generalized level compared to the mobile cost being incurred operating such services in particular and a mobile business in general. For an in-depth and beautiful treatment of mobile revenues past, present and future, I would like to refer to Chetan Sharma’s 2012 paper “Operator’s Dilemma (and Opportunity): The 4th Wave” (note: you can download the paper by following the link in the html article) on mobile revenue dynamics from (1) Voice (1st Revenue or Service Wave), (2) Messaging (2nd Revenue or Service Wave) to todays (3) Access (3rd Revenue Wave) and the commence to what Chetan Sharma defines as the 4th Wave of Revenues (note: think of waves as S-curves describing initial growth spurt, slow down phase, stagnation and eventually decline) which really describes a collection of revenue or service waves (i.e., S-curves) representing a portfolio of Digital Services, such as (a) Connected Home, (b) Connected Car, (c) Health, (d) Payment, (e) Commerce, (f) Advertising, (g) Cloud Services (h) Enterprise solutions, (i) Identity, Profile & Analysis etc.. I feel confident that adding any Digital Service enabled by Internet-of-Things (IoT) and M2M would be important inclusions to the Digital Services Wave. Given the competition (i.e., Facebook, Google, Amazon, Ebay, etc..) that mobile operators will face entering the 4th Wave of Digital Services Space, in combination with having only national or limited international scale, will make this area a tough challenge to return direct profit on. The inherent limited international or national-only scale appears to be one of the biggest barrier to turn many of the proposed Digital Services, particular with those with strong Social Media Touch Points, into meaningful business opportunities for mobile operators.
This said, I do believe (strongly) that Telecom Operators have very good opportunities for winning Digital Services Battles in areas where their physical infrastructure (including Spectrum & IT Architecture) is an asset and essential for delivering secure, private and reliable services. Local regulation and privacy laws may indeed turn out to be a blessing for Telecom Operators and other national-oriented businesses. The current privacy trend and general consumer suspicion of American-based Global Digital Services / Social Media Enterprises may create new revenue opportunities for national-focused mobile operators as well as for other national-oriented digital businesses. In particular if Telco Operators work together creating Digital Services working across operator’s networks, platforms and beyond (e.g., payment, health, private search, …) rather than walled-garden digital services, they might become very credible alternatives to multi-national offerings. It is highly likely that consumers would be more willing to trust national mobile operator entities with her or his personal data & money (in fact they already do that in many areas) than a multinational social-media corporation. In addition to the above Digital Services, I do expect that Mobile/Telecom Operators and Entertainment Networks (e.g., satellite, cable, IP-based) will increasingly firm up partnerships as well as acquire & merge their businesses & business models. In all effect this is already happening.
For emerging growth markets without extensive and reliable fixed broadband infrastructures, high-quality (& likely higher cost compared to today’s networks!) mobile broadband infrastructures would be essential to drive additional Digital Services and respective revenues as well as for new entertainment business models (other than existing Satellite TV). Anyway, Chetan captures these Digital Services (or 4th Wave) revenue streams very nicely and I recommend very much to read his articles in general (i.e., including “Mobile 4th Wave: The Evolution of the Next Trillion Dollars” which is the 2nd “4th Wave” article).
Back to mobile profitability and how to ensure that the mobile business model doesn’t breakdown as revenue growth starts to slow down and decline while the growth of mobile cost overtakes the revenue growth.
A good friend of mine, who also is a great and successful CFO, stated that “Profitability is rarely a problem to achieve (in the short term)”;“I turn down my market invest (i.e., OpEx) and my Profitability (as measured in terms of EBITDA) goes up. All I have done is getting my business profitable in the short term without having created any sustainable value or profit by this. Just engineered my bonus.”
Our aim must be to ensure sustainable and stable profitability. This can only be done by understanding, careful managing and engineering our basic Telco cost structures.
While most Telco’s tend to plan several years ahead for Capital Expenditures (CapEx) and often with a high degree of sophistication, the same Telco’s mainly focus on one (1!) year ahead for OpEx. Further effort channeled into OpEx is frequently highly simplistic and at times in-consistent with the planned CapEx. Obviously, in the growth phase of the business cycle one may take the easy way out on OpEx and focus more on the required CapEx to grow the business. However, as the time-scales for committed OpEx “lives” on a much longer period than Revenue (particular Prepaid Revenue or even CapEx for that matter), any shortfall in Revenue and Profitability will be much more difficult to mitigate by OpEx measures that takes time to become effective. In markets with little or no market investment the penalty can be even harsher as there is no or little OpEx cushion that can be used to soften a disappointing direction in profitability.
How come a telecom business in Asia, or other emerging growth markets around the world, can maintain, by European standards, such incredible high EBITDA Margins. Margin’s that run into 50s or even higher. Is this “just” a matter of different lower-cost & low GDP economies? Does the higher margins simply reflect a different stage in the business cycle (i.e., growth versus super-saturation)?, Should Mature Market really care too much about Emerging Growth Markets? in the sense of whether Mature Markets can learn anything from Emerging Growth Markets and maybe even vice versa? (i.e., certainly mature markets have made many mistakes, particular when shifting gears from growth to what should be sustainability).
Before all those questions have much of a meaning, it might be instructive to look at the differences between a Mature Market and an Emerging Growth Market. I obviously would not have started this Blog, unless I believe that there are important lessons to be had by understanding what is going on in both types of markets. I also should make it clear that I am only using the term Emerging Growth Markets as most of the markets I study is typically defined as such by economists and consultants. However from a mobile technology perspective few of those markets we tend to call Emerging Growth Markets can really be called emerging any longer and growth has slowed down a lot in most of those markets. This said, from a mobile broadband perspective most of the markets defined in this analysis as Emerging Growth Markets are pretty much dead on that definition.
Whether the emerging markets really should be looking forward to mobile broadband data growth might depend a lot on whether you are the consumer or the provider of services.
For most Mature Markets the introduction of 3G and mobile broadband data heralded a massive slow-down and in some cases even decline in revenue. This imposed severe strains on Mobile Margins and their EBITDAs. Today most mature markets mobile operators are facing a negative revenue growth rate and is “forced” continuously keep a razor focus on OpEx, Mitigating the revenue decline keeping Margin and EBITDA reasonably in check.
Emerging Markets should as early as possible focus on their operational expenses and Optimize with a Vengeance.
Well well let ‘s get back to the comparison and see what we can learn!
It doesn’t take to long to make a list of some of the key and maybe at times obvious differentiators (not intended to be exhaustive) between Mature and Emerging Markets are;
Side Note: it should be clear that by today many of the markets we used to call emerging growth markets are from mobile telephony penetration & business development certainly not emerging any longer and as growing as they were 5 or 10 years ago. This said from a 3G/4G mobile broadband data penetration perspective it might still be fair to characterize those markets as emerging and growing. Though as mature markets have seen that journey is not per se a financial growth story.
Looking at the above table we can assess that Firstly: the straightforward (and possible naïve) explanation of relative profitability differences between Mature and Emerging Markets, might be that emerging markets cost structures are much more favorable compared to what we find in mature market economies. Basically the difference between Low and High GDP economies. However, we should not allow ourselves to be too naïve here as lessons learned from low GDP economies are that some cost structure elements (e.g., real estate, fuel, electricity, etc..) are as costly (some times more so) than what we find back in mature high/higher GDP markets. Secondly: many emerging growth market’s economies are substantially more populous & dense than what we find in mature markets (i.e., although it is hard to beat Netherlands or the Ruhr Area in Germany). Maybe the higher population count & population density leads to better scale than can be achieved in mature markets. However, while maybe true for the urban population, emerging markets tend to have substantially higher ratio of their population living in rural areas compared to what we find in mature markets. Thirdly: maybe the go-to-market approach in emerging markets is different from mature markets (e.g., subsidies, quality including network coverage, marketing,…) offering substantially lower mobile quality overall compared to what is the practice in mature markets. Providing poor mobile network quality certainly have been a recurring theme in the Philippines mobile industry despite the Telco Industry in Philippines enjoys Margins that most mature markets operators can only dream of. It is pretty clear that for 3G-UMTS based mobile broadband, 900 MHz does not have sufficient bandwidth to support the anticipated mobile broadband uptake in emerging markets (e.g., particular as 900MHz is occupied by 2G-GSM as well). IF emerging markets mobile operators will want to offer mobile data at reasonable quality levels (i.e., and the IF is intentional), sustain anticipated customer demand and growth they are likely to require network densification (i.e., extra CapEx and OpEx) at 2100 MHz. Alternative they might choose to wait for APT 700 MHz and drive an affordable low-cost LTE device ecosystem albeit this is some years ahead.
More than likely some of the answers of why emerging markets have a much better margins (at the moment at least) will have to do with cost-structure differences combined with possibly better scale and different go-to-market requirements more than compensating the low revenue per user.
Let us have a look at the usual suspects towards the differences between mature & emerging markets. The EBITDA can be derived as Revenue minus the Operational Expenses (i.e., OpEx) and the corresponding margin is Ebitda divided by the Revenue (ignoring special accounting effects that here);
The EBITDA & Margin tells us in absolute and relative terms how much of our Revenue we keep after all our Operational expenses (i.e., OpEx) have been paid (i.e., beside tax, interests, depreciation & amortization charges).
We can write Revenue as a the product of ARPU (Average Number of Users) times Number of Users N and thus the EBITDA can also be written as;
. We see that even if ARPU is low (or very) low, an Emerging Market with lot of users might match the Revenue of a Mature Market with higher ARPU and worse population scale (i.e., lower amount of users). Pretty simple!
But what about the Margin? , in order for an Emerging Market to have substantially better Margin than corresponding Mature Market at the same revenue level, it is clear that the Emerging Market’s OpEx (O) needs to be lower than that of a Mature markets. We also observe that if the Emerging Market Revenue is lower than the Mature Market, the corresponding Opex needs to be even lower than if the Revenues were identical. One would expect that lower GDP countries would have lower Opex (or Cost in general) combined with better population scale is really what makes for a great emerging market mobile Margins! … Or is it ?
A Small but essential de-tour into Cost Structure.
Some of the answers towards the differences in margin between mature and emerging markets obviously lay in the OpEx part or in the Cost-structure differences. Let’s take a look at a mature market’s cost structure (i.e., as you will find in Western & Eastern Europe) which pretty much looks like this;
With the following OpEx or cost-structure elements;
Usage-related OpEx:typically take up between 10% to 35% of of the total OpEx with an average of ca. 25%. On average this OpEx contribution is approximately 17% of the revenue in mature European markets. Trend wise it is declining. Usage-based OpEx is dominated by interconnect & roaming voice traffic and to a less degree of data interconnect and peering. In a scenario where there is little circuit switched voice left (i.e., the ultimate LTE scenario) this cost element will diminish substantially from the operators cost structure. It should be noted that this also to some extend is being influenced by regulatory forces.
Market Invest: can be decomposed into Subscriber Acquisition Cost (SAC), i.e., “bribing” the customers to leave your competitor for yourself, Subscriber Retention Cost (SRC), i.e., “bribing” your existing (valuable) customers to not let them be “bribed” by you’re a competitor and leave you (i.e., churn), and lastly Other Marketing spend for advertisement, promotional and so forth. This cost-structure element contribution to OpEx can vary greatly depending on the market composition. In Europe’s mature markets it will vary from 10% to 31% with a mean value of ca. 23% of the total OpEx. On average it will be around 14% of the Revenue. It should be noted that as the mobile penetration increases and enter into heavy saturation (i.e., >100%), SAC tends to reduce and SRC will increase. Further in markets that are very prepaid heavy SAC and SRC will naturally be fairly minor cost structure element (i.e., 10% of OpEx or lower and only a couple of % of Revenue). Profit and Margin can rapidly be influenced by changes in the market invest. SAC and SRC cost-structure elements will in general be small in emerging growth markets (compared to corresponding mature markets).
Terminal-equipment related OpEx: is the cost associated by procuring terminals equipment (i.e, handsets, smartphones, data cards, etc.). In the past (prior to 2008) it was fairly common that OpEx from procuring and revenues from selling terminals were close to a zero-sum game. In other words the cost made for the operator of procuring terminals was pretty much covered by re-selling them to their customer base. This cost structure element is another heavy weight and vary from 10% to 20% of the OpEx with an average in mature European markets of 17%. Terminal-related cost on average amounts to ca. 11% of the Revenue (in mature markets). Most operators in emerging growth markets don’t massively procure, re-sell and subsidies handsets, as is the case in many mature markets. Typically handsets and devices in emerging markets will be supplied by a substantial 2nd hand gray and black market readily available.
Personnel Cost: amounts to between 6% to 15% of the Total OpEx with a best-practice share of around the 10%. The ones who believe that this ratio is lower in emerging markets might re-think their impression. In my experience emerging growth markets (including the ones in Eastern & Central Europe) have a lower unit personnel cost but also tends to have much larger organizations. This leads to many emerging growth markets operators having a personnel cost share that is closer to the 15% than to the 10% or lower. On average personnel cost should be below 10% of revenue with best practice between 5% and 8% of the Revenue.
Technology Cost (Network & IT):includes all technology related OpEx for both Network and Information Technology. Personnel-related technology OpEx (prior to capitalization ) is accounted for in the above Personnel Cost Category and would typically be around 30% of the personnel cost pending on outsourcing level and organizational structure. Emerging markets in Central & Eastern Europe historical have had higher technology related personnel cost than mature markets. In general this is attributed to high-quality relative low-cost technology staff leading to less advantages in outsourcing technology functions. As Technology OpEx is the most frequent “victim” of efficiency initiatives, lets just have a look at how the anatomy of the Technology Cost Structure looks like:
Technology Cost (Network & IT) – continued: Although, above Chart (i.e., taken from my 2012 Keynote at the Broadband MEA 2012, Dubai “Ultra-efficient network factory: Network sharing and other means to leapfrog operator efficiencies”) emphasizes a Mature Market View, emerging markets cost distribution does not differ that much from the above with a few exceptions. In Emerging Growth Markets with poor electrification rates diesel generators and the associated diesel full will strain the Energy Cost substantially. As the biggest exposure to poor electrical grid (in emerging markets) in general tend to be in Rural and Sub-Urban areas it is a particular OpEx concern as the emerging market operators expands towards Rural Areas to capture the additional subscriber potential present there. Further diesel fuel has on average increased with 10% annually (i..e, over the least 10 years) and as such is a very substantial Margin and Profitability risk if a very large part of the cellular / mobile network requires diesel generators and respective fuel. Obviously, “Rental & Leasing” as well as “Service & Maintenance” & “Personnel Cost” would be positively impacted (i.e., reduced) by Network Sharing initiatives. Best practices Network Sharing can bring around 35% OpEx savings on relevant cost structures. For more details on benefits and disadvantages (often forgotten in the heat of the moment) see my Blog “The ABC of Network Sharing – The Fundamentals”. In my experience one of the greatest opportunities in Emerging Growth Markets for increased efficiency are in the Services part covering Maintenance & Repair (which obviously also incudes field maintenance and spare part services).
Other Cost: typically covers the rest of OpEx not captured by the above specific items. It can also be viewed as overhead cost. It is also often used to “hide” cost that might be painful for the organization (i.e., in terms of authorization or consequences of mistakes). In general you will find a very large amount of smaller to medium cost items here rather than larger ones. Best practices should keep this below 10% of total OpEx and ca. 5% of Revenues. Much above this either means mis-categorization, ad-hoc projects, or something else that needs further clarification.
So how does this help us compare a Mature Mobile Market with an Emerging Growth Market?
As already mentioned in the description of the above cost structure categories particular Market Invest and Terminal-equipment Cost are items that tend to be substantially lower for emerging market operators or entirely absent from their cost structures.
Lets assume our average mobile operator in an average mature mobile market (in Western Europe) have a Margin of 36%. In its existing (OpEx) cost structure they spend 15% of Revenue on Market Invest of which ca. 53% goes to subscriber acquisition (i.e., SAC cost category), 40% on subscriber retention (SRC) and another 7% for other marketing expenses. Further, this operator has been subsidizing their handset portfolio (i.e., Terminal Cost) which make up another 10% of the Revenue.
Our Average Operator comes up with the disruptive strategy to remove all SAC and SRC from their cost structure and stop procuring terminal equipment. Assuming (and that is a very big one in a typical western European mature market) that revenue remains at the same level, how would this average operator fare?
Removing SAC and SRC, which was 14% of the Revenue will improve the Margin adding another 14 percentage points. Removing terminal procurement from its cost structure leads to an additional Margin jump of 10 percentage points. The final result is a Margin of 60% which is fairly close to some of the highest margins we find in emerging growth markets. Obviously, completely annihilating Market Invest might not be the most market efficient move unless it is a market-wide initiative.
Albeit the example might be perceived as a wee bit academic, it serves to illustrate that some of the larger margin differences we observe between mobile operators in mature and emerging growth markets can be largely explain by differences in the basic cost structure, i..e, the lack of substantial subscriber acquisition and retention costs as well as not procuring terminals does offer advantages to the emerging market business model.
However, it also means that many operators in emerging markets have little OpEx flexibility, in the sense of faster OpEx reduction opportunities once mobile margin reduces due to for example slowing revenue growth. This typical becomes a challenge as mobile penetration starts reaching saturation and as ARPU reduces due to diminishing return on incremental customer acquisition.
There is not much substantial OpEx flexibility (i..e, market invest & terminal procurement) in Emerging Growth Markets mobile accounts. This adds to the challenge of avoiding profitability squeeze and margin exposure by quickly scaling back OpEx.
This is to some extend different from mature markets that historically had quiet a few low hanging fruits to address before OpEx efficiency and reduction became a real challenge. Though ultimately it does become a challenge.
Back to Profitability with a Vengeance.
So it is all pretty simple! … leave out Market Invest and Terminal Procurement … Then add that we typically have to do with Lower GDP countries which conventional wisdom would expect also to have lower Opex (or Cost in general) combined with better population scale .. isn’t that really what makes for a great emerging growth market Mobile Margin?
Hmmm … Albeit Compelling ! ? … For the ones (of us) who would think that the cost would scale nicely with GDP and therefor a Low GDP Country would have a relative Lower Cost Base, well …
In the Chart above the Y-axis is depicted with logarithmic scaling in order to provide a better impression of the data points across the different economies. It should be noted that throughout the years 2007 to 2013 (note: 2013 data is shown above) there is no correlation between a countries mobile Opex, as estimated by Revenue – EBITDA, and the GDP.
Well … GDP really doesn’t provide the best explanation (to say the least)! … So what does then?
I have carried out multi-linear regression analysis on the available data from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” datasets between the years 2007 to 2013. The multi-linear regression approach is based on year-by-year analysis of the data with many different subsets & combination of data chosen including adding random data.
I find that the best description (R-square 0.73, F-Ratio of 30 and p-value(s) <0.0001) of the 48 country’s data on Opex. The amount of data points used in the multi-regression is at least 48 for each parameter and that for each of the 7 years analyzed. The result of the (preliminary) analysis is given by the following statistically significant parameters explaining the Mobile Market OpEx:
Population – The higher the size of the population, proportional less Mobile Market Opex is spend (i.e., scale advantage).
Penetration – The higher the mobile penetration, proportionally less Mobile Market Opex is being spend (i.e., scale advantage and the incremental penetration at an already high penetration would have less value thus less Opex should be spend).
Users (i..e., as measured by subscriptions) – The more Users the higher the Mobile Market Opex (note: prepaid ratio has not been found to add statistical significance).
ARPU (Average Revenue Per User) – The higher the ARPU, the higher will the Mobile Market Opex be.
If I leave out ARPU, GDP does enter as a possible descriptive candidate although the overall quality of the regression analysis suffers. However, it appears that the GDP and ARPU cannot co-exist in the analysis. When Mobile Market ARPU data are included, GDP becomes non-significant. Furthermore, a countries Surface Area, which I previously believed would have a sizable impact on a Mobile Market’s OpEx, also does not enter as a significant descriptive parameter in this analysis. In general the Technology related OpEx is between 15% to 25% (maximum) of the Total OpEx and out that possible 40% to 60% would be related to sites that would be needed to cover a given surface area. This might no be significant enough in comparison to the other parameters or simply not a significant factor in the overall country related mobile OpEx.
I had also expected 3G-UMTS to have had a significant contribution to the Opex. However this was not very clear from the analysis either. Although in the some of the earlier years (2005 – 2007), 3G does enter albeit not with a lot of weight. In Western Europe most incremental OpEx related to 3G has been absorb in the existing cost structure and very little (if any) incremental OpEx would be visible particular after 2007. This might not be the case in most Emerging Markets unless they can rely on UMTS deployments at 900 MHz (i.e., the traditional GSM band). Also the UMTS 900 solution would only last until capacity demand require the operators to deploy UMTS 2100 (or let their customers suffer with less mobile data quality and keep the OpEx at existing levels). In rural areas (already covered by GSM at 900 MHz) the 900 MHz UMTS deployment option may mitigate incremental OpEx of new site deployment and further encourage rural active network sharing to allow for lower cost deployment and providing rural populations with mobile data and internet access.
The Population Size of a Country, the Mobile Penetration and the number of Users and their ARPU (note last two basically multiplies up to the revenue) are most clearly driving a mobile markets Opex.
Philippines versus Germany – Revenue, Cost & Profitability.
Philippines in 2013 is estimated to have a population of ca. 100 Million compared to Germany’s ca. 80 Million. The Urban population in Germany is 75% taking up ca. 17% of the German surface area (ca. 61,000 km2 or a bit more than Croatia). Comparison this to Philippines 50% urbanization that takes up up only 3% (ca. 9,000 km2 or equivalent to the surface area of Cyprus). Germany surface area is about 20% larger than Philippines (although the geographies are widely .. wildly may be a better word … different, with the Philippines archipelago comprising 7,107 islands of which ca. 2,000 are inhabited, making the German geography slightly boring in comparison).
In principle if all I care about is to cover and offer services to the urban population (supposedly the ones with the money?) I only need to cover 9 – 10 thousand square kilometer in the Philippines to capture ca. 50 Million potential mobile users (or 5,000 pop per km2), while I would need to cover about 6 times that amount of surface area to capture 60 million urban users in Germany (or 1,000 pop per km2). Even when taking capacity and quality into account, my Philippine cellular network should be a lot smaller and more efficient than my German mobile network. If everything would be equal, I basically would need 6 times more sites in Germany compared to Philippines. Particular if I don’t care too much about good quality but just want to provide best effort services (that would never work in Germany by the way). Philippines would win any day over Germany in terms of OpEx and obviously also in terms of capital investments or CapEx. It does help the German Network Economics that the ARPU level in Germany is between 4 times (in 2003) to 6 times (in 2013) higher than in Philippines. Do note that the two major Germany mobile operators covers almost 100% of the population as well as most of the German surface area and that with a superior quality of voice as well as mobile broadband data. This does not true hold true for Philippines.
In 2003 a mobile consumer in Philippines would spend on average almost 8 US$ per month for mobile services. This was ca. 4x lower than a German customer for that year. The 2003 ARPU of the Philippines roughly corresponded to 10% of the GDP per Capita versus 1.2% of the German equivalent. Over the 10 Years from 2003 to 2013, ARPU dropped 60% in Philippine and by 2013 corresponded to ca. 1.5% of GDP per Capita (i.e., a lot more affordable propositions). The German 2013 ARPU to GDP per Capita ratio was 0.5% and its ARPU was ca. 40% lower than in 2003.
The Philippines ARPU decline and Opex increase over the 10 year period led to a Margin drop from 64% to 45% (19% drop!) and their Margin is still highly likely to fall further in the near to medium-term. Despite the Margin drop Philippines still made a PHP26 Billion more EBITDA in 2013 than compared to 2003 (ca. 45% more or equivalent compounded annual growth rate of 3.8%).
Germany had ca. 3x more mobile subscribers compared to Philippines.
German Mobile Revenue was 14x higher than Philippines.
German EBITDA was 9x higher than that of Philippines.
German OpEx was 23x higher than that of Philippines Mobile Industry.
Mobile Margin of the Philippines was 64% versus 42% of Germany.
Germany’s GPD per Capita (in US$) was 35 times larger than that of Philippines.
Germany’s mobile ARPU was 4 times higher than that of Philippines.
in 2013 (+ 10 Years)
Philippines & Germany have almost the same amount of mobile subscriptions.
Germany Mobile Revenue was 6x higher than Philippines.
German EBITDA was only 5x higher than that of Philippines.
German OpEx was 6x higher than Mobile OpEx in Philippines (and German OpEx was at level with 2003).
Mobile Margin of the Philippines dropped 19% to 45% compared to 42% of Germany (essential similar to 2003).
In local currencies, Philippines increased their EBITDA with ca. 45%, Germany remain constant.
Both Philippines and Germany has lost 11% in absolute EBITDA between the 10 Year periods maximum and 2013.
Germany’s GDP per Capita (in US$) was 14 times larger than that of the Philippines.
Germany’s ARPU was 6 times higher than that of Philippines.
In the Philippines, mobile revenues have grown with 7.4% per anno (between 2003 and 2013) while the corresponding mobile OpEx grew with 12% and thus eroding margin massively over the period as increasingly more mobile customers were addressed. In Philippines, the 2013 OpEx level was 3 times that of 2003 (despite one major network consolidation and being an essential duopoly after the consolidation). In Philippines over this period the annual growth rate of mobile users were 17% (versus Germany’s 6%). In absolute terms the number of users in Germany and Philippines were almost the same in 2013, ca. 115 Million versus 109 Million. In Germany over the same period Financial growth was hardly present although more than 50 Million subscriptions were added.
When OpEx grows faster than Revenue, Profitability will suffer today & even more so tomorrow.
Mobile capital investments (i.e., CapEx) over the period 2003 to 2013 was for Germany 5 times higher than that of Philippines (i.e., remember that Germany also needs at least 5 – 6 times more sites to cover the Urban population) and tracks at a 13% Capex to Revenue ratio versus Philippines 20%.
The stories of Mobile Philippines and of Mobile Germany are not unique. Likewise examples can be found in Emerging Growth Markets as well as Mature Markets.
Can Mature Markets learn or even match (keep on dreaming?) from Emerging Markets in terms of efficiency? Assuming such markets really are efficient of course!
As logic (true or false) would dictate given the relative low ARPUs in emerging growth markets and their correspondingly high margins, one should think that such emerging markets are forced to run their business much more efficient than in Mature Markets. While compelling to believe this, the economical data would indicate that most emerging growth markets have been riding the subscriber & revenue growth band wagon without too much thoughts to the OpEx part … and Frankly why should you care about OpEx when your business generates margins much excess of 40%? Well … it is (much) easier to manage & control OpEx year by year than to abruptly “one day” having to cut cost in panic mode when growth slows down the really ugly way and OpEx keeps increasing without a care in the world. Many mature market operators have been in this situation in the past (e.g., 2004 – 2008) and still today works hard to keep their margins stable and profitability from declining.
Most Companies will report both Revenues and EBITDA on quarterly and annual basis as both are key financial & operational indicators for growth. They tend not report Opex but as seen from above that’s really not a problem to estimate when you have Revenue and EBITDA (i.e., OpEx = Revenue – EBITDA).
Thus, had you left the European Telco scene (assuming you were there in the first place) for the last 10 years and then came back you might have concluded that not much have happened in your absence … at least from a profitability perspective. Germany was in 2013 almost at its Ebitda margin level of 2003. Of course as the ones who did not take a long holiday knows those last 10 years were far from blissful financial & operational harmony in the mature markets where one efficiency program after the other struggled to manage, control and reduce Operators Operational Expenses.
However, over that 10-year period Germany added 50+ Million mobile subscriptions and invested more than 37 Billion US$ into the mobile networks from T-Deutschland, Vodafone, E-plus and Telefonica-O2. The mobile country margin over the 10-year period has been ca. 43% and the Capex to Revenue ratio ca. 13%. By 2013 the total amount of mobile subscription was in the order of 115 Million out of a population of 81 Million (i.e., 54 Million of the German population is between 15 and 64 years of age). The observant numerologist would have realized that there are many more subscriptions than population … this is not surprising as it reflects that many subscribers are having multiple different SIM cards (as opposed to cloned SIMs) or subscription types based on their device portfolio and a host of other reasons.
All Wunderbar! … or? .. well not really … Take a look at the revenue and profitability over the 10 year period and you will find that no (or very very little) revenue and incremental profitability has been gained over the period from 2003 to 2013. AND we did add 80+% more subscriptions to the base!
Here is the Germany Mobile development over the period;
Apart from adding subscribers, having modernized the mobile networks at least twice over the period (i.e, CapEx with little OpEx impact) and introduced LTE into the German market (i.e., with little additional revenue to show for it) not much additional value has been added. It is however no small treat what has happen in Germany (and in many other mature markets for that matter). Not only did Germany almost double the mobile customers (in terms of subscriptions), over the period 3G Nodes-B’s were over-layed across the existing 2G network. Many additional sites were added in Germany as the fundamental 2G cellular grid was primarily based on 900 MHz and to accommodate the higher UMTS frequency (i.e., 2100 MHz) more new locations were added to provide a superior 3G coverage (and capacity/quality). Still Germany managed all this without increasing the Mobile Country OpEx across the period (apart from some minor swings). This has been achieved by a tremendous attention to OpEx efficiency with every part of the Industry having razor sharp attention to cost reduction and operating at increasingly efficiency.
Philippines story is a Fabulous Story of Growth (as summarized above) … and of Profitability & Margin Decline.
Philippines today is in effect a duopoly with PLDT having approx. 2/3 of the mobile market and Globe the remaining 1/3. During the period the Philippine Market saw Sun Cellular being acquired and merged by PLDT. Further, 3G was deployed and mobile data launched in major urban areas. SMS revenues remained the largest share of non-voice revenue to the two remaining mobile operators PLDT and Globe. Over the period 2003 to 2013, the mobile subscriber base (in terms of subscriptions) grew with 16% per anno and the ARPU fell accordingly with 10% per anno (all measured in local currency). All-in-all safe guarding a “healthy” revenue increase over the period from ca. 93 Billion PHP in 2003 to 190 Billion PHP in 2013 (i.e., a 65% increase over the period corresponding to a 5% annual growth rate).
However, the Philippine market could not maintain their relative profitability & initial efficiency as the mobile market grew.
So we observe (at least) two effects (1) Reduction in ARPU as market is growing & (2) Increasing Opex cost to sustain the growth in the market. As more customers are added to a mobile network the return on thus customers increasingly diminishes as network needs to be massively extended capturing the full market potential versus “just” the major urban potential.
Mobile Philippines did become less economical efficient as its scale increases and ARPU dropped (i.e., by almost 70%). This is not an unusual finding across Emerging Growth Markets.
As I have described in my previous Blog “SMS – Assimilation is inevitable, Resistance is Futile!”, Philippines mobile market has an extreme exposure to SMS Revenues which amounts to more than 35% of Total Revenues. Particular as mobile data and smartphones penetrate the Philippine markets. As described in my previous Blog, SMS Services enjoy the highest profitability across the whole range of mobile services we offer the mobile customer including voice. As SMS is being cannibalized by IP-based messaging, the revenue will decline dramatically and the mobile data revenue is not likely to catch up with this decline. Furthermore, profitability will suffer as the the most profitable service (i.e., SMS) is replaced by mobile data that by nature has a different profitability impact compared to simple SMS services.
Philippines do not only have a substantial Margin & EBITDA risk from un-managed OpEx but also from SMS revenue cannibalization (a la KPN in the Netherlands and then some).
Let us compare the ARPU & Opex development for Philippines (above Chart) with that of Germany over the same period 2003 to 2013 (please note that the scale of Opex is very narrow)
Mobile Germany managed their Cost Structure despite 40+% decrease in ARPU and as another 60% in mobile penetration was added to the mobile business. Again similar trend will be found in most Mature Markets in Western Europe.
One may argue (and not being too wrong) that Germany (and most mature mobile markets) in 2003 already had most of its OpEx bearing organization, processes, logistics and infrastructure in place to continue acquiring subscribers (i.e., as measured in subscriptions). Therefor it have been much easier for the mature market operators to maintain their OpEx as they continued to grow. Also true that many emerging mobile markets did not have the same (high) deployment and quality criteria, as in western mature markets, in their initial network and service deployment (i.e., certainly true for the Philippines as is evident from the many Regulatory warnings both PLDT and Globe received over the years) providing basic voice coverage in populated areas but little service in sub-urban and rural areas.
Most of the initial emerging market networks has been based on coarse (by mature market standards) GSM 900 MHz (or CDMA 850 MHz) grids with relative little available capacity and indoor coverage in comparison to population and clutter types (i.e., geographical topologies characterized by their cellular radio interference patterns). The challenge is, as an operator wants to capture more customers, it will need to build out / extend its mobile network in the areas those potential or prospective new customers live and work in. From a cost perspective sub-urban and rural areas in emerging markets are not per se lower cost areas despite such areas in general being lower revenue areas than their urban equivalents. Thus, as more customers are added (i.e., increased mobile penetration) proportionally more cost are generated than revenue being capture and the relative margin will decline. … and this is how the Ugly-cost (or profitability tail) is created.
I just cannot write about profitability and cost structure without throwing the Ugly-(cost)-Tail on the page.I strongly encourage all mobile operators to make their own Ugly-Tail analysis. You will find more details of how to remedy this Ugliness from your cost structure in “The ABC of Network Sharing – The Fundamentals”.
In Western Europe’s mature mobile markets we find that more than 50% of our mobile cellular sites captures no more than 10% of the Revenues (but we do tend to cover almost all surface area several times unless the mobile operators have managed to see the logic of rural network sharing and consolidated those rural & sub-urban networks). Given emerging mobile markets have “gone less over board” in terms of lowest revenue un-profitable network deployments in rural areas you will find that the number of sites carrying 10% of less of the revenue is around 40%. It should be remembered that the rural populations in emerging growth markets tend to be a lot larger than in of that in mature markets and as such revenue is in principle spread out more than what would be the case in mature markets.
Population & Mobile Statistics and Opex Trends.
The following provides a 2013 Summary of Mobile Penetration, 3G Penetration (measured in subscriptions), Urban Population and the corresponding share of surface area under urban settlement. Further to guide the eye the 100% line has been inserted (red solid line), a red dotted line that represents the share of the population that is between 15 and 64 years of age (i.e., who are more likely to afford a mobile service) and a dashed red line providing the average across all the 43 countries analyzed in this Blog.
Sources: United Nations, Department of Economic & Social Affairs, Population Division. The UN data is somewhat outdated though for most data points across emerging and mature markets changes have been minor. Mobile Penetration is based on Pyramid Research and Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line).
There is a couple of points (out of many) that can be made on the above data;
There are no real emerging markets any longer in the sense of providing basic mobile telephone services such as voice and messaging.
For mobile broadband data via 3G-UMTS (or LTE for that matter), what we tend to characterize as emerging markets are truly emerging or in some case nascent (e.g., Algeria, Iraq, India, Pakistan, etc..).
All mature markets have mobile penetration rates way above 100% with exception of Canada, i.e., 80% (i.e., though getting to 100% in Canada might be a real challenge due to a very dispersed remaining 20+% of the population).
Most emerging markets are by now covering all urban areas and corresponding urban population. Many have also reach 100% mobile penetration rates.
Most Emerging Markets are lagging Western Mature Markets in 3G penetration. Even providing urban population & urban areas with high bandwidth mobile data is behind that of mature markets.
Size & density does matter … in all kind of ways when it comes to the economics of mobile networks and the business itself.
In Australia I only need to cover ca. 40 thousand km2 (i.e., 0.5% of the total surface area and a bit less than the size of Denmark) to have captured almost 90% of the Australian population (e.g., Australia’s total size is 180+ times that of Denmark excluding Greenland). I frequently hear my Australian friends telling me how Australia covers almost 100% of the population (and I am sure that they cover more area than is equivalent to Denmark too) … but without being (too) disrespectful that record is not for Guinness Book of Records anytime soon. in US (e.g., 20% more surface area than Australia) I need to cover in almost 800 thousand km2 (8.2% of surface area or equivalent to a bit more than Turkey) to capture more than 80% of the population. In Thailand I can only capture 35% of the population by covering ca. 5% of the surface area or a little less than 30 thousand km2 (approx. the equivalent of Belgium). The remaining of 65% of the Thai population is rural-based and spread across a much larger surface area requiring extensive mobile network to provide coverage to and capture additional market share outside the urban population.
So in Thailand I might need a bit less cell sites to cover 35% of my population (i.e., 22M) than in Australia to cover almost 90% of the population (i.e., ca. 21M). That’s pretty cool economics for Australia which is also reflected in a very low profitability risk score. For Thailand (and other countries with similar urban demographics) it is tough luck if they want to reach out and get the remaining 65% of their population. The geographical dispersion of the population outside urban areas is very wide and increasing geographical area is required to be covered in order to catch this population group. UMTS at 900 MHz will help to deploy economical mobile broadband, as will LTE in the APT 700 MHz band (being it either FDD Band 28 or TDD Band 44) as the terminal portfolio becomes affordable for rural and sub-urban populations in emerging growth markets.
In Western Europe on average I can capture 77% of my population (i..e, the urban pop) covering 14.2% of the surface area (i.e., average over markets in this analysis), This is all very agreeable and almost all Western European countries cover their surface areas to at least 80% and in most cases beyond that (i.e., it’s just less & easier land to cover though not per see less costly). In most cases rural coverage is encourage (or required) by the mature market license regime and not always a choice of the mobile operators.
Before we look in depth to the growth (incl. positive as well as negative growth), lets first have a peek at what has happened to the mobile revenue in terms of ARPU and Number of Mobile User and the corresponding mobile penetration over the period 2007 to 2013.
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data were used to calculated the growth of ARPU as compounded annual growth rate between 2007 to 2013 and the annual growth rate between 2012 and 2013. Since 2007 the mobile ARPUs have been in decline and to make matters worse the decline has even accelerated rather than slowed down as markets mobile penetration saturated.
Source: Mobile Penetrations taken from Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data .Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line). It s interesting to observe that most emerging growth markets are now where the mature markets were in 2007 in terms of mobile penetration.
Apart from a very few markets, ARPU has been in a steady decline since 2007. Further in many countries the ARPU decline has even accelerated rather than slowed down. From most mature markets the conclusion that we can draw is that there are no evidence that mobile broadband data (via 3G-UMTS or LTE) has had any positive effect on ARPU. Although some of the ARPU decline over the period in mature markets (particular European Union countries) can be attributed to regulatory actions. In general as soon a country mobile penetration reaches 100% (in all effect reaches the part of the population 15-64 years of age) ARPU tends to decline faster rather than slowing down. Of course one may correctly argue that this is not a big issue as long as the ARPU times the Users (i.e., total revenue) remain growing healthily. However, as we will see that is yet another challenge for the mobile industry as also the total revenue in mature markets also are in decline on a year by year basis. Given the market, revenue & cost structures of emerging growth markets, it is not unlikely that they will face similar challenges to their mobile revenues (and thus profitability). This could have a much more dramatic effect on their overall mobile economics & business models than what has been experienced in the mature markets which have had a lot more “cushion” on the P&Ls to defend and even grow (albeit weakly) their profitability. It is instructive to see that the most emerging growth markets mobile penetrations have reached the levels of Mature Markets in 2007. Combined with the introduction and uptake of mobile broadband data this marks a more troublesome business model phase than what these markets have experienced in the past.Some of the emerging growth market have yet to introduce 3G-UMTS, and some to leapfrog mobile broadband by launching LTE. Both events, based on lessons learned from mature markets, heralds a more difficult business model period of managing cost structures while defending revenues from decline and satisfy customers appetite for mobile broadband internet that cannot be supported by such countries fixed telecommunications infrastructures.
For us to understand more profoundly where our mobile profitability is heading it is obviously a good idea to understand how our Revenue and OpEx is trending. In this Section I am only concerned about the Mobile Market in Country and not the individual mobile operators in the country. For that latter (i.e., Operator Profitability) you will find a really cool and exiting analytic framework in the Section after this. I am also not interested (in this article) in modeling the mobile business bottom up (been there & done that … but that is an entirely different story line). However, I am interested and I am hunting for some higher level understanding and a more holistic approach that will allow me to probabilistically (by way of Bayesian analysis & ultimately inference) to predict in which direction a given market is heading when it comes to Revenue, OpEx and of course the resulting EBITDA and Margin. The analysis I am presenting in this Section is preliminary and only includes compounded annual growth rates as well as the Year-by-Year growth rates of Revenue and OpEx. Further developments will include specific market & regulatory developments as well to further improve on the Bayesian approach. Given the wealth of data accumulated over the years from the Bank of America Merrill Lynch (BoAML) Global Wireless Matrix datasets it is fairly easy to construct & train statistical models as well as testing those consistent with best practices.
The Chart below comprises 48 countries Revenue & OpEx growth rates as derived from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” dataset (note: BoAML data available in this analysis goes back to 2003). Out of the 48 Countries, 23 countries have an Opex compounded annual growth rate higher than the corresponding Revenue growth rate. Thus, it is clear that those 23 countries are having a higher risk of reduced margin and strained profitability due to over-proportionate growth of OpEx. Out of the 23 countries with high or very high profitability risk, 11 countries have been characterized in macro-economical terms as emerging growth markets (i.e., China, India, Indonesia, Philippines, Egypt, Morocco, Nigeria, Russia, Turkey, Chile, Mexico) the remaining 12 countries can be characterized as mature markets in macro-economical terms (i.e., New Zealand, Singapore, Austria, Belgium, France, Greece, Spain, Canada, South Korea, Malaysia, Taiwan, Israel). Furthermore, 26 countries had a higher Opex growth between 2012 and 2013 than their revenues and is likely to be trending towards dangerous territory in terms of Profitability Risk.
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. The Compounded Annual Growth Rate (CAGR) is calculated with X being Revenue and Opex. Y-axis scale is from -25% to +25% (i.e., similar to the scale chosen in the Year- by-Year growth rate shown in the Chart below).
With few exceptions one does not need to read the countries names on the Chart above to immediately see where we have the Mature Markets with little or negative growth and where what we typically call emerging growth markets are located.
As the above Chart clearly illustrate the mobile industry across different types of markets have an increasing challenge to deliver profitable growth and if the trend continues to keep their profitability period!
Opex grows faster than Mobile Operator’s can capture Revenue … That’s a problem!
In order gauge whether the growth dynamics of the last 7 years is something to be concerned about (it is! … it most definitely is! but humor me!) … it is worthwhile to take a look at the year by year growth rate trends (i.e. as CAGR only measures the starting point and the end point and “doesn’t really care” about what happens in the in-between years).
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. Year on Year growth is calculated and is depicted in the Chart above. Y-axis scale is from -25% to +25%. Note that the Y-scales in the Year-on-Year Growth Chart and the above 7-Year CAGR Growth Chart are the same and thus directly comparable.
From the Year on Year Growth dynamics compared to the compounded 7-year annual growth rate, we find that Mature Markets Mobile Revenues decline has accelerated. However, in most cases the Mature Market OpEx is declining as well and the Control & Management of the cost structure has improved markedly over the last 7 years. Despite the cost structure management most Mature Markets Revenue have been declining faster than the OpEx. As a result Profitability Squeeze remains a substantial risk in Mature Markets in general.
In almost all Emerging Growth Markets the 2013 to 2012 revenue growth rate has declined in comparison with the compounded annual growth rate. Not surprising as most of those markets are heading towards 100% mobile penetration (as measured in subscriptions). OpEx growth remains a dire concern for most of the emerging growth markets and will continue to squeeze emerging markets profitability and respective margins. There is no indication (in the dataset analyzed) that OpEx is really under control in Emerging Growth Markets, at least to the same degree as what is observed in the Mature Markets (i.e., particular Western Europe). What further adds to the emerging markets profitability risk is that mobile data networks (i.e., 3G-UMTS, HSPA+,..) and corresponding mobile data uptakes are just in its infancy in most of the Emerging Growth Markets in this analysis. The networks required to sustain demand (at a reasonable quality) are more extensive than what was required to provide okay-voice and SMS. Most of the emerging growth markets have no significant fixed (broadband data) infrastructure and in addition poor media distribution infrastructure which can relieve the mobile data networks being built. Huge rural populations with little available ARPU potential but a huge appetite to get connected to internet and media will further stress the mobile business models cost structure and sustainable profitability.
This argument is best illustrated by comparing the household digital ecosystem evolution (or revolution) in Western Europe with the projected evolution of Emerging Growth Markets.
Above Chart illustrates the likely evolution in Home and Personal Digital Infrastructure Ecosystem of an emerging market’s Household (HH). Particular note that the amount of TV Displays are very low and much of the media distribution is expected to happen over cellular and wireless networks. An additional challenge is that the fixed broadband infrastructure is widely lagging in many emerging markets (in particular in sub-urban and rural areas) increasing the requirements of the mobile network in those markets. It is compelling to believe that we will witness a completely different use case scenarios of digital media consumption than experienced in the Western Mature Markets. The emerging market is not likely to have the same degree of mobile/cellular data off-load as experienced in mature markets and as such will strain mobile networks air-interface, backhaul and backbone substantially more than is the case in mature markets. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
Same as above but projection for Western Europe. In comparison with Emerging Markets a Mature Market Household (HH) has many more TV as wells as a substantially higher fixed broadband penetration offering high-bandwidth digital media distribution as well as off-load optionality for mobile devices via WiFi. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
Mobile Market Profit Sustainability Risk Index
The comprehensive dataset from Bank of America Merrill Lynch Global Wireless Matrix allows us to estimate what I have chosen to call a Market Profit Sustainability Risk Index. This Index provides a measure for the direction (i.e., growth rates) of Revenue & Opex and thus for the Profitability.
The Chart below is the preliminary result of such an analysis limited to the BoAML Global Wireless Matrix Quarter 1 of 2014. I am currently extending the Bayesian Analysis to include additional data rather than relying only on growth rates of Revenue & Opex, e.g., (1) market consolidation should improve the cost structure of the mobile business, (2) introducing 3G usually introduces a negative jump in the mobile operator cost structure, (3) mobile revenue growth rate reduces as mobile penetration increases, (4) regulatory actions & forces will reduce revenues and might have both positive and negative effects on the relevant cost structure, etc.…
So here it is! Preliminary but nevertheless directionally reasonable based on Revenue & Opex growth rates, the Market Profit Sustainability Risk Index over for 48 Mature & Emerging Growth Markets worldwide:
The above Market Profit Sustainability Risk Index is using the following risk profiles
Very High Risk (index –5): (i.e., for margin decline): (i) Compounded Annual Growth Rate (CAGR) between 2007 and 2013 of Opex was higher than equivalent for Revenue AND (ii) Year-on-Year (YoY) Growth Rate 2012 to 2013 of Opex higher than that of Revenue AND (iii) Opex Year-on-Year 2012 to 2013 Growth Rate is higher than the Opex CAGR over the period 2007 to 2013.
High Risk (index –3): Same as above Very High Risk with condition (iii) removed ORYoY Revenue Growth 2012 to 2013 lower than the corresponding Opex Growth.
Medium Risk (index –2): CAGR of Revenue lower than CAGR of Opex but last year (i.e., 2012 t0 2013) growth rate of Revenue higher than that of Opex.
Low Risk (index 1): (i) CAGR of Revenue higher than CAGR of Opex AND (ii) YoY Revenue Growth higher than Opex Growth but lower than the inflation of the previous year.
Very Low Risk (index 3): Same as above Low Risk with YoY Revenue Growth Rate required to be higher than the Opex Growth with at least the previous year’s inflation rate.
The Outlook for Mature Markets are fairly positive as most of those Market have engaged in structural cost control and management for the last 7 to 8 years. Emerging Growth Markets Profit Sustainability Risk Index are cause for concern. As the mobile markets are saturating it usually results in lower ARPU and higher cost to reach the remaining parts of the population (often “encouraged” by regulation). Most Emerging Growth markets have started to introduce mobile data, which is likely to result in higher cost-structure pressure & with traditional revenue streams under pressure (if history of Mature Markets are to repeat itself in emerging growth markets). The Emerging Growth Markets have had little incentive (in the past) to focus on cost structure control and management, due to the exceedingly high margins that they historically could present with their legacy mobile services (i.e., Voice & SMS) and relative light networks (as always in comparison to Mature Markets).
Cautionary note is appropriate. All the above are based on the Mobile Market across the world. There are causes and effects that can move a market from having a high risk profile to a lower. Even if I feel that the dataset supports the categorization it remains preliminary as more effects should be included in the current risk model to add even more confidence in its predictive power. Furthermore, the analysis is probabilistic in nature and as such does not claim to carve in stone the future. All the Index claims to do is to indicate a probable direction of the profitability (as well as Revenue & OpEx). There are several ways that Operators and Regulatory Authorities might influence the direction of the profitability changing Risk Exposure (in the Wrong as well as in the Right Direction)
Furthermore, it would be wrong to apply the Market Profit Sustainability Risk Index to individual mobile operators in the relevant markets analyzed here. The profitability dynamics of individual mobile operators are a wee bit more complicated, albeit some guidelines and predictive trends for their profitability dynamics in terms of Revenue and Opex can be defined. This will all be revealed in the following Section.
Operator Profitability – the Profitability Math.
We have seen that the Margin M an be written as
with E, R and O being EBITDA, REVENUE and OPEX respectively.
However, much more interesting is that it can also be written as a function of subscriber share
being valid forwith being the margin and the subscriber market share can be found between 0% to 100%. The rest will follow in more details below, suffice to say that as the subscriber market share increases the Margin (or relative profitability) increases as well although not linearly (if anyone would have expected that ).
Before we get down and dirty on the math lets discuss Operator Profitability from a higher level and in terms of such an operators subscriber market share (i.e., typically measured in subscriptions rather than individual users).
In the following I will show some Individual Operator examples of EBITDA Margin dynamics from Mature Markets limited to Western Europe. Obviously the analysis and approach is not limited emerging markets and can (have been) directly extended to Emerging Growth Markets or any mobile market for that matter. Again BoAML Global Matrix provides a very rich data set for applying the approach described in this Blog.
It has been well established (i.e., by un-accountable and/or un-countable Consultants & Advisors) that an Operator’s Margin correlates reasonably well with its Subscriber Market Share as the Chart below illustrates very well. In addition the Chart below also includes the T-Mobile Netherlands profitability journey from 2002 to 2006 up to the point where Deutsche Telekom looked into acquiring Orange Netherlands. An event that took place in the Summer of 2007.
I do love the above Chart (i.e., must be the physicist in me?) as it shows that such a richness in business dynamics all boiled down to two main driver, i.e., Margin & Subscriber Market Shared.
So how can an Operator strategize to improve its profitability?
Let us take an Example
Here is how we can think about it in terms of Subscriber Market Share and EBITDA as depicted by the above Chart. In simple terms an Operator have a combination of two choices (Bullet 1 in above Chart) Improve its profitability through Opex reductions and making its operation more efficient without much additional growth (i.e., also resulting in little subscriber acquisition cost), it can improve its ARPU profile by increasing its revenue per subscriber (smiling a bit cynical here while writing this) again without adding much in additional market share. The first part of Bullet 1 has been pretty much business as usual in Western Europe since 2004 at least (unfortunately very few examples of the 2nd part of Bullet 1) and (Bullet 2 in above Chart) The above “Margin vs. Subscriber Market Share” Chart indicates that if you can acquire the customers of another company (i.e., via Acquisition & Merger) it should be possible to quantum leap your market share while increasing the efficiencies of the operation by scale effects. In the above Example Chart our Hero has ca. 15% Customer Market Share and the Hero’s target ca. 10%. Thus after an acquisition our Hero would expect to get ca. 25% (if they play it well enough). Similarly we would expect a boost in profitability and hope for at least 38% if our Hero has 18% margin and our Target has 20%. Maybe even better as the scale should improve this further. Obviously, this kind of “math” assumes that our Hero and Target can work in isolation from the rest of the market and that no competitive forces would be at play to disrupt the well thought through plan (or that nothing otherwise disruptive happens in parallel with the merger of the two businesses). Of course such a venture comes with a price tag (i.e., the acquisition price) that needs to be factored into the overall economics of acquiring customers. As said most (Western) Operators are in a perpetual state of managing & controlling cost to maintain their Margin, protect and/or improve their EBITDA.
So one thing is theory! Let us see how the Dutch Mobile Markets Profitability Dynamics evolved over the 10 year period from 2003 to 2013;
From both KPN’s acquisition of Telfort as well as the acquisition & merger of Orange by T-Mobile above Margin vs. Subscriber Market Share Chart, we see that in general, the Market Share logic works. On the other hand the management of the integration of the business would have been fairly unlucky for that to be right. When it comes to the EBITDA logic it does look a little less obvious. KPN clearly got unlucky (if un-luck has something to do with it?) as their margin decline with a small uplift albeit still lower than where they started pre-acquisition. KPN should have expected a margin lift to 50+%. That did not happen to KPN – Telfort. T-Mobile did fare better although we do observe a margin uplift to around 30% that can be attributed to Opex synergies resulting from the integration of the two businesses. However, it has taken many Opex efficiency rounds to get the Margin up to 38% that was the original target for the T-Mobile – Orange transaction.
In the past it was customary to take lots of operators from many countries, plot their margin versus subscriber markets share, draw a straight line through the data points and conclude that the margin potential is directly related to the Subscriber Market Share. This idea is depicted by the Left Side Chart and the Straight line “Best” Fit to data.
Lets just terminate that idea … it is wrong and does not reflect the right margin dynamics as a function of the subscriber markets share. Furthermore, the margin dynamics is not a straight-line function of the subscriber market share but rather asymptotic falling off towards minus infinity, i.e., when the company have no subscribers and no revenue but non-zero cost. We also observed a diminishing return on additional market share in the sense that as more market share is gained smaller and smaller incremental margins are gained. The magenta dashed line in the Left Chart below illustrates how one should expect the Margin to behave as a function of Subscriber market share.
The Right Chart above shows has broken down the data points in country by country. It is obvious that different countries have different margin versus market share behavior and that drawing a curve through all of those might be a bit naïve.
So how can we understand this behavior? Let us start with making a very simple formula a lot more complex :–)
We can write the Marginas the ratio of Earning before Interest Tax Depreciation & Amortization (EBITDA)and Revenue R:, EBITDA is defined as Revenue minus Opex. Both Opex and Revenue I can de-compose into a fixed and a variable part: O = Of + AOPU x U and R = Rf + ARPU x U with AOPU being the Average Opex per User, ARPU the Average (blended) Revenue per User and U the number of users. For the moment I will be ignoring the fixed part of the revenue and write R = ARPU x U. Further, the number of users can be written as with being the market share and M being the market size. So we can now write the margin as
with and .
being valid for
The Margin is not a linear function of the Subscriber Market Share (if anybody would have expected that) but relates to the Inverse of Market Share.
Still the Margin becomes larger as the market share grows with maximum achievable margin of as the market share equals 1 (i.e., Monopoly). We observe that even in a Monopoly there is a limit to how profitable such a business can be. It should be noted that this is not a constant but a function of how operationally efficient a given operator is as well as its market conditions. Furthermore, as the market share reduces towards zero .
Fixed Opex (of) per total subscriber market: This cost element is in principle related to cost structure that is independent on the amount of customers that a given mobile operator have. For example a big country with a relative low population (or mobile penetration) will have higher fixed cost per total amount of subscribers than a smaller country with a larger population (or mobile penetration). Fixed cost is difficult to change as it depends on the network and be country specific in nature. For an individual Operator the fixed cost (per total market subscribers) will be influenced by;
Coverage strategy, i.e., to what extend the country’s surface area will be covered, network sharing, national roaming vs. rural coverage, leased bandwidth, etc..
Spectrum portfolio, i.e, lower frequencies are more economical than higher frequencies for surface area coverage but will in general have less bandwidth available (i.e., driving up the number of sites in capacity limited scenarios). Only real exception to bandwidth limitations of low frequency spectrum would be the APT700 band (though would “force” an operator to deploy LTE which might not be timed right given specifics of the market).
General economical trends, lease/rental cost, inflation, salary levels, etc..
Average Variable Opex per User (ou): This cost structure element capture cost that is directly related to the subscriber, such as
Any other variable cost directly associated with the customer (e.g., customer facing functions in the operator organization).
This behavior is exactly what we observe in the presented Margin vs. Subscriber Market Share data and also explains why the data needs to be treated on a country by country basis. It is worthwhile to note that after the higher the market share the less incremental margin gain should be expected for additional market share.
The above presented profitability framework can be used to test whether a given mobile operator is market & operationally efficient compared to its peers.
The overall Margin dynamics is shown above Chart for the various settings of fixed and variable Opex as well as a given operators ARPU. We see that as the fixed Opex (in relation to the total subscriber market) increasing it will get more difficult to get EBITDA positive and increasingly more market share is required to reach a reasonable profitability targets. The following maps a 3 player market according with the profitability logic derived here:
What we first notice is that operators in the initial phase of what you might define as the “Market-share Capture Phase” are extremely sensitive to setbacks. A small loss of subscriber market share (i.e. 2%) can tumble the operator back into the abyss (i.e, 15% Margin setback) and wreck havoc to the business model. The profitability logic also illustrates that once an operator has reached Market-share maturity adding new subscribers is less valuable than to keep them. Even big market share addition will only result in little additional profitability (i.e., the law of diminishing returns).
The derived Profitability framework can be used also to illustrate what happens to the Margin in a market-wise steady situation (i.e., only minor changes to an operators market share) or what the Market Share needs to be to keep a given Margin or how cost needs to be controlled in the event that ARPU drops and we want to keep our margin and cannot grow market share (or any other market, profitability or cost-structure exercise for that matter);
Above chart illustrates Margin as a function of ARPU & Cost (fixed & variable) Development at a fixed market share here chosen to be 33%. The starting point is an ARPU ru of EUR25.8 per month, a variable cost per user ou assumed to be EUR15 and a fixed cost per total mobile user market (of) of EUR0.5. The first scenario (a Orange Solid Line) with an end of period margin of 32.7% assumes that ARPU reduces with 2% per anno, that the variable cost can be controlled and likewise will reduce with 2% pa. Variable cost is here assumed to increase with 3% on an annual basis. During the 10 year period it is assumed that the Operators market share remains at 33%. The second scenario (b Red Dashed Line) is essential same as (a) with the only difference that the variable cost remains at the initial level of EUR15 and will not change over time. This scenario ends at 21.1% after 10 Years. In principle it shows that Mobile Operators will not have a choice on reducing their variable cost as ARPU declines (again the trade-off between certainty of cost and risk/uncertainty of revenue). In fact the most successful mature mobile operators are spending a lot of efforts to manage & control their cost to keep their margin even if ARPU & Revenues decline.
The above chart illustrates what market share is required to keep the margin at 36% when ARPU reduces with 2% pa, fixed cost increases with 3% pa and the variable cost either (a Orange Solid Line) can be reduced with 2% in line with the ARPU decline or (b Red Solid Line) remains fixed at the initial level. In scenario (a) the mobile operator would need to grow its market share to 52% to main its margin at 36%. This will obviously be very challenging as this would be on the expense of other operators in this market (here assume to be 3). Scenario (b) is extremely dramatic and in my opinion mission impossible as it requires a complete 100% market dominance.
Above Chart illustrates how we need to manage & control my variable cost compared to the –2% decline pa in order to keep the Margin constant at 36% assuming that the Operator Subscriber Market Share remains at 33% over the period. The Orange Solid Line in the Chart shows the –2% variable cost decline pa and the Red Dashed Line the variable cost requirement to keep the margin at 36%.
The following illustrates the Profitability Framework as described above applied to a few Western European Markets. As this only serves as an illustration I have chosen to show older data (i..e, 2006). It is however very easy to apply the methodology to any country and the BoAML Global Wireless Matrix with its richness in data can serve as an excellent source for such analysis. Needless to say the methodology can be extended to assess an operators profitability sensitivity to market share and market dynamics in general.
The Charts below shows the Equalized Market Share which simply means the fair market share of operators, i.e., if I have 3 operators the fair or equalized market share would 1/3 (33.3%), in case of 4 operators it should be 25% and so forth, I am also depicting what I call the Max Margin Potential this is simply the Margin potential at 100% Market Share at a given set of ARPU (ru), AOPU (ou) and Fixed Cost (of) Level in relation to the total market.
Netherlands Chart: Equalized Market Share assumes Orange has been consolidated with T-Mobile Netherlands. The analysis would indicate that no more than ca. 40% Margin should be expected in The Netherlands for any of the 4 Mobile Operators. Note that for T-Mobile and Orange small increases in market share should in theory lead to larger margins, while KPN’s margin would be pretty much un-affected by additional market share.
Germany Chart: Shows Vodafone to slightly higher and T-Mobile Deutschland slight lower in Margin than the idealized Margin versus Subscriber Market share. At the time T-Mobile had almost exclusive leased lines and outsourced their site infrastructure while Vodafone had almost exclusively Microwaves and owned its own site infrastructure. The two new comers to the German market (E-Plus and Telefonica-O2) is trailing on the left side of the Equalized Market Share. At this point in time should Telefonica and E-Plus have merged one would have expected them eventually (post-integration) to exceed a margin of 40%. Such a scenario would lead to an almost equilibrium market situation with remaining 3 operators having similar market shares and margins.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. I certainly have not always been very present during the analysis and writing.
Up-to 50% of Sites in Mobile Networks captures no more than 10% of Mobile Service Revenues.
The “Ugly” (cost) Tail of Cellular Networks can only be remedied by either removing sites (and thus low- or –no-profitable service) or by aggressive site sharing.
With Network Sharing expect up-to 35% saving on Technology Opex as well as future Opex avoidance.
The resulting Technology Opex savings easily translates into a Corporate Opex saving of up-to 5% as well as future Opex avoidance.
Active as well as Passive Network Sharing brings substantial Capex avoidance and improved sourcing economics by improved scale.
National Roaming can be an alternative to Network Sharing in low traffic and less attractive areas. Capex attractive but a likely Ebitda-pressure point over time.
“Sharing by Towerco” can be an alternative to Real Network Sharing. It is an attractive mean to Capex avoidance but is not Ebitda-friendly. Long-term commitments combined with Ebitda-risks makes it a strategy that should to be considered very carefully.
Network Sharing frees up cash to be spend in other areas (e.g., customer acquisition).
Network Sharing structured correctly can result in faster network deployment –> substantial time to market gains.
Network Sharing provides substantially better network quality and capacity for a lot less cash (compared to standalone).
Instant cell split option easy to realize by Network Sharing –> cost-efficient provision of network capacity.
Network Sharing offers enhanced customer experience by improved coverage at less economics.
Network Sharing can bring spectral efficiency gains of 10% or higher.
The purpose of this story is to provide decision makers, analysts and general public with some simple rules that will allow them to understand Network Sharing and assess whether it is likely to be worthwhile to implement and of course successful in delivering the promise of higher financial and operational efficiency.
Today’s Technology supports almost any network sharing scenario that can be thought of (or not). Financially & not to forget Strategically this is far from so obvious.
Network Sharing is not only about Gains, its evil twin Loss is always present.
Network Sharing is a great pre-cursor to consolidation.
Network sharing has been the new and old black for many years. It is a fashion that that seems to stay and grow with and within the telecommunications industry. Not surprising as we shall see that one of the biggest financial efficiency levers are in the Technology Cost Structure. Technology wise there is no real stumbling blocks for even very aggressive network sharing maximizing the amount of system resources being shared, passive as well as active. The huge quantum-leap in availability of very high quality and affordable fiber optic connectivity in most mature markets, as well between many countries, have pushed the sharing boundaries into Core Network, Service Platforms and easily reaching into Billing & Policy Platforms with regulatory and law being the biggest blocking factor of Network-as-a-Service offerings. Below figure provides the anatomy of network sharing. It should of course be noted that also within each category several flavors of sharing is possible pending operator taste and regulatory possibilities.
Network Sharing comes in many different flavors. To only consider one sharing model is foolish and likely will result in wrong benefit assessment. Setting a sharing deal up for failure down the road (if it ever gets started). It is particular important to understand that while active sharing provides the most comprehensive synergy potential, it tends to be a poor strategy in areas of high traffic potential. Passive sharing is a much more straightforward strategy in such areas. In rural areas, where traffic is less of an issue and profitability is a huge challenge, aggressive active sharing is much more interesting. One should even consider frequency sharing if permitted by regulatory authority. The way I tend to look at the Network Sharing Flavors are (as also depicted in the Figure below);
Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
Coverage Limited Areas (i.e., some urban environments, mainly sub-urban and rural) – Minimum Passive Sharing should be pursued with RAN (Active) Sharing providing an additional economical advantage.
Rural Areas – National Roaming or Full RAN sharing including frequency sharing (if regulatory permissible).
One of the first network sharing deals I got involved in was back in mid-2001 in The Netherlands. This was at the time of the Mobile Industry’s first real cash crises. Just as we were about to launch this new exiting mobile standard (i.e., UMTS) that would bring Internet to the pockets of the masses. After having spend billions & billions of dollars (i.e., way too much of course) on high-frequency 2100MHz UMTS spectrum, all justified by an incredible optimistic (i.e., said in hindsight!) belief in the mobile internet business case, the industry could not afford to deploy the networks required to make our wishful thinking come true.
T-Mobile (i.e., aka Ben BV) engaged with Orange (i.e., aka Dutchtone) in The Netherlands on what should have been a textbook example of the perfect network sharing arrangement. We made a great business case for a comprehensive network sharing. It made good financial and operational sense at the setup. At the time the sharing game was about Capex avoidance and trying to get the UMTS network rolled out as quickly as possible within very tight budgets imposed by our mother companies (i.e., Deutsche Telekom and France Telecom respectively). Two years down the road we revised our strategic thoughts on network sharing. We made another business case for why deploying on standalone made more sense than sharing. At that time the only thing T-we (Mobile NL) really could agree with Orange NL about was ancillary cabinet sharing and of course the underlying site sharing. Except for agreeing not to like the Joint Venture we created (i.e., RANN BV), all else were at odds, e.g., supplier strategy, degree of sharing, network vision, deployment pace, etc… Our respective deployment strategies had diverged so substantially from each other that sharing no longer was an option. Further, T-Mobile decided to rely on the ancillary cabinet we had in place for GSM –> so also no ancillary sharing. This was also at a time where cabinets and equipment took up a lot of space (i.e., do you still remember the first & 2nd generation 3G cabinets?). Many site locations simply could not sustain 2 GSM and 2 UMTS solutions. Our site demand went through the roof and pretty much killed the sharing case.
Starting point: Site Sharing, Shared Built, Active RAN and transport sharing.
Just before breakup I: Site Sharing, cabinet sharing if required, shared built where deployment plans overlapped.
Just before breakup II:Crisis over and almost out. Cash and Capex was no longer as critical as it was at startup.
It did not help that the Joint Venture RANN BV created to realize T-Mobile & Orange NL shared UMTS network plans frequently were at odds with both founding companies. Both entities still had their full engineering & planning departments including rollout departments (i.e., in effect we tried to coordinate across 3 rollout departments & 3 planning departments, 1 from T-Mobile, 1 from Orange and 1 from RANN BV … pretty silly! Right!). Eventually RANN BV was dissolved. The rest is history. Later T-Mobile NL acquired Orange NL and engaged in a very successful network consolidation (within time and money).
The economical benefits of Sharing and Network Consolidation are pretty similar and follows pretty much the same recipe.
Luckily (if Luck has anything to do with it?) since then there have been more successful sharing projects although the verdict is still out whether these constructs are long-lived or not and maybe also by what definition success is measured.
Judging from the more than 34 Thousand views on my various public network sharing presentations, I have delivered around the world since 2008, there certainly seem to be a strong and persistent interest in the topic.
I have worked on Network Sharing and Cost Structure Engineering since the early days of 2001. Very initially focus was on UMTS deployments, the need and requirements to deploy much more cash efficient. Cash was a very scarce resource after the dot-com crash between 2000 & 2003. After 2004 the game changed to be an Opex Saving & Avoidance game to mitigate stagnating customer growth and revenue growth slow down.
I have in detail studied many Network Sharing strategies, concepts and deals. A few have turned out successful (at least still alive & kicking) and many more un-successful (never made it beyond talk and analysis). One of the most substantial Network Sharing deals (arguable closer to network consolidation), I work on several years ago is still very much alive and kicking. That particular setup has been heralded as successful and a poster-boy example of the best of Network Sharing (or consolidation). However, by 2014 there has hardly been any sites taken out of operation (certainly no where close to the numbers we assumed and based our synergy savings on).
More than 50% of all network related TCO comes from site-related operational and capital expenses.
Despite the great economical promises and operational efficiencies that can be gained by two mobile operations (fixed for that matter as well) agreeing to share their networks, it is important to note that
It is NOT enough to have a great network sharing plan. A very high degree of discipline and razor-sharp focus in project execution is crucial for delivering network sharing within money and time.
With introduction of UMTS & Mobile Broadband the mobile operator’s margin & cash have come under increasing pressure (not helped by voice revenue decline & saturated markets).
Technology addresses up-to 25% of a Mobile Operators Total Opex & more than 90% of the Capital Expenses.
Radio Access Networks accounts easily for more than 50% of all Network Opex and Capex.
For a reasonable efficient Telco Operation, Technology Cost is the most important lever to slow the business decline, improve financial results and return on investments.