The Nature of Telecom Capex – a 2023 Update.


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.


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 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:

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 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

CV \; = \; \frac{FCFF_0 \; (1 \; + \; g)}{\; WACC \; - \; g \; }

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?);

CV_X \; - \; CV_Y \; = \; \Delta Capex \; \left[ \frac{1 \; - \; g}{\; WACC \; - \; g \;} \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.


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.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mobile Access Capex.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Fixed Access Capex.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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;

C_{total} \; = \; C_{mobile} \; + \; C_{fixed}

\; = \; \alpha_{customers}^{mobile} \; N_{customers}^{mobile} \; + \; \alpha_{revenue}^{mobile} \; R_{revenue}^{mobile}

\; +  \;  \beta_{customers}^{fixed} \; N_{customers}^{fixed} \; + \; \beta_{revenue}^{fixed} \; R_{revenue}^{fixed}

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

C_{fixed} \; = \;  \beta_{customers}^{fixed} \; N_{customers}^{fixed} \; + \; \beta_{revenue}^{fixed} \; R_{revenue}^{fixed}

C_{mobile} \; = \; C_{total} \; - \; C_{fixed}

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.


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.

N_{t+1} \; = \; N_t \; + \; \Delta N_{t+1}

d_{t+1} \; = \; d_t \; + \; \Delta d_{t+1}

D_{t+1}^{total} \; = \; N_{t+1} \times d_{t+1}

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 (T_{demand} or T_{BH}) is the direct driver of network load, and thus, network investments, the volumetric demand, is a manifestation of that throughput demand.

T_{demand} \; = \; T_{BH \; in \; bits/sec} \; max_t \sum_{cell} \; n_t^{cell} \; \times \; 8 \; \delta_t^{cell} \; = \; max_t \sum_{cell} \; \tau_t^{cell}

With n_t^{cell} being the number of active users in a given radio cell at the time-instant of unit t taken within a day. \delta_t^{cell} is the Bytes consumed in a time instant (e.g., typically a second); thus, 8 \delta_t^{cell}  gives us the bits per time unit (or bits/sec), which is throughput consumed. Sum over all the cells’ instant throughput (\tau_t^{cell} 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 (T_{supply}) given in bits per second, the basic building block of quality, is relatively easy to estimate. The cellular throughput (per unit cell) is provided by the amount of committed frequency spectrum to the air interface, what your radio access network and antenna support are, multiplied by the so-called spectral efficiency in bits per Hz per cell. The spectral efficiency depends on the antenna technology and the underlying software implementation of signal processing schemes enabling the details of receiving and sending signals over the air interface.

T_{supply} can be written as follows;

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

For example, if we have a site that covers 3 cells (or sectors) with a deployed 100 MHz @ 3.6GHz (B) on a 32T32R advanced antenna system (AAS) with an effective downlink (i.e., from the antenna to user), spectral efficiency \eta_{eff} of ca. 20 Mbps/MHz/cell (i.e., \eta_{eff} = n_{eff} \times \eta_{SISO}), 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.


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

The ABC of Network Sharing – The Fundamentals (Part I).

  • 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.

anatomy of network sharing

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);

  1. Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
  2. 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.
  3. 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.

  1. Fundamentals of Mobile Network Sharing.(2012).
  2. Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. (2012).
  3. Economics of Network Sharing. (2008).
  4. Technology Cost Optimization Strategies. (2009).
  5. Analyzing Business Models for Network Sharing Success. (2009).

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 mobilenetworksharingtco 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.

P&L Optimization

Above Profit & Loss Figure serves as an illustration that Technology Cost (Opex & Capex) optimization and is pivotal to achieve a more efficient operation and a lot more certain that relying on new business (and revenue) additions

It is not by chance that RAN Sharing is such a hot topic. The Radio Access Network takes up more than half of Network Cost including Capex.

Of course there are many other general cost levers to consider that might be less complex than Network Sharing to implement. Another Black (or Dark Grey) is outsourcing of (key) operational functions to a 3rd party. Think here about some of the main ticks

  1. Site acquisition (SA) & landlord relations (LR) – Standard practice for SA, not recommended for landlord relations. Usually better done by operator self (at least while important during deployment)..
  2. Site Build – Standard practice with sub-contractors..
  3. Network operations & Maintenance – Cyclic between in-source and outsource pending business cycle.
  4. Field services – standard practice particular in network sharing scenarios.
  5. Power management – particular interesting for network sharing scenarios with heavy reliance of diesel generators and fuel logistics (also synergetic with field services).
  6. Operational Planning – particular for comprehensive managed network services. Network Sharing could outsource RAN & TX Planning.
  7. Site leases – Have a site management company deal with site leases with a target to get them down with x% (they usually take a share of the reduced amount). Care should be taken not to jeopardize network sharing possibilities. Will impact landlord relations.
  8. IT operations – Cyclic between in-source and outsource pending business cycle.
  9. IT Development – Cyclic between in-source and outsource pending business cycle.
  10. Tower Infrastructure – Typical Cash for infrastructure swap with log-term Opex commitments. Care must be taken to allow for Network Sharing and infrastructure termination.

In general many of the above (with exception of IT or at least in a different context than RAN Sharing) potential outsourcing options can be highly synergetic with Network Sharing and should always be considered when negotiating a deal.

Looking at the economics of managed services versus network sharing we find in general the following picture;


and remember that any managed services that is assumed to be applicable in the Network Sharing strategy  column will enable the upper end of the possible synergy potential estimated. Having a deeper look at the original T-Mobile UK and Hutchinson UK 3G RAN Sharing deal is very instructive as it provides a view on what can be achieved when combining both best practices of network sharing and shared managed services (i.e., this is the story for The ABC of Network Sharing – Part II).

Seriously consider Managed Services when it can be proven to provide at least 20% Opex synergies will be gained for apples to apples SLAs and KPIs (as compared to your insourced model).

Do your Homework! It is bad Karma to implement Managed Services on an in-efficient organizational function or area that has not been optimized prior to outsourcing.

Do your Homework (Part II)! Measure, Analyze and Understand your own relevant cost structure 100% before outsourcing!

It is not by chance that Deutsche Telekom AG (DTAG) has been leading the Telco Operational Efficiency movement and have some of the most successful network sharing operations around. Since 2004 DTAG have had several (very) deep dives programs into their cost structure and defining detailed initiatives across every single operation as well as on its Group level. This has led to one of the most efficient Telco operations around in Western Europe & the US and with lots to learn from when it comes to managing your cost structure when faced with stagnating revenue growth and increasing cost pressure.

In 2006, prior to another very big efficiency program was kicked off within DTAG, I was asked to take a very fundamental and extreme (but nevertheless realistic) look at all the European mobile operations technology cost structures and come back with how much Technology Opex could be pulled out of them (without hurting the business) within 3-4 years (or 2010).

Below (historical) Figure illustrates my findings from 2006 (disguised but nevertheless the real deal);


This analysis (7-8 years old by now) directly resulted in a lot of Network Sharing discussions across DTAGs operations in Europe. Ultimately this work led to a couple of successful Network Sharing engagements within the DTAG (i.e., T-Mobile) Western European footprint. It enabled some of the more in-efficient mobile operations to do a lot more than they could have done standalone and at least one today went from a number last to number 1. So YES … Network Sharing & Cost Structure Engineering can be used to leapfrog an in-efficient business and by that transforming an ugly duckling into what might be regarded as an approximation of a swan. (in this particular example I have in mind, I will refrain from calling it a beautiful swan … because it really isn’t … although the potential is certainly remain even more today).

The observant reader till see that the order of things (or cost structure engineering) matters. As already said above, the golden rule of outsourcing and managed services is to first ensure you have optimized what can be done internally and then consider outsourcing. We found that first outsourcing network operations or establish a managed service relationship prior to a network sharing relationship was sub-optimal and actually might be hindering reaching the most optimal network sharing outcome (i.e., full RAN sharing or active sharing with joint planning & operations).


Revenue Growth will eventually slow down and might even decline due to competitive climate, poor pricing management and regulatory pressures, A Truism for all markets … its just a matter of time. The Opex Growth is rarely in synch with the revenue slow down. This will result in margin or Ebitda pressure and eventually profitability decline.

Revenue will eventually stagnate and likely even enter decline. Cost is entropy-like and will keep increasing.

The technology refreshment cycles are not only getting shorter. These cycles imposes additional pressure on cash. Longer return on investment cycles results compared to the past. Paradoxical as the life-time of the Mobile Telecom Infrastructure is shorter than in the past. This vicious cycle requires the industry to leapfrog technology efficiency, driving demand for infrastructure sharing and business consolidation as well as new innovative business models (i.e., a topic for another Blog).

The time Telco’s have to return on new technology investments is getting increasingly shorter.

Cost saving measures are certain by nature. New Business & New (even Old) Revenue is by nature uncertain.


I have probably learned more from the network sharing deals that failed than the few ones that succeeded (in the sense of actually sharing something). I have work on sharing deals & concepts across across the world; in Western Europe, Central Eastern Europe, Asia and The USA under very different socio-economical conditions, financial expectations, strategic incentives, and very diverse business cycles.

It is fair to say that over the time I have been engaged in Network Sharing Strategies and Operational Realities, I have come to the conclusion that the best or most efficient sharing strategy depends very much on where an operator’s business cycle is and the network’s infrastructure age.

The benefits that potentially can be gained from sharing will depend very much on whether you

  • Greenfield: Initial phase of deployment with more than 80% of sites to be deployed.
  • Young: Steady state with more than 80% of your sites already deployed.
  • Mature: Just in front of major modernization of your infrastructure.

The below Figure describes the three main cycles of network sharing.


It should be noted that I have omitted the timing benefit aspects from the Rollout Phase (i.e., Greenfield) in the Figure above. The omission is on purpose. I believe (based on experience) that there are more likelihood of delay in deployment than obvious faster time-to-market. This is inherent in getting everything agreed as need to be agreed in a Greenfield Network Sharing Scenario. If time-to-market matters more than initial cost efficiency, then network sharing might not a very effective remedy. Once launch have been achieved and market entry secured, network sharing is an extremely good remedy in securing better economics in less attractive areas (i.e., typical rural and outer sub-urban areas). There are some obvious and very interesting games that can be played out with your competitor particular in the Rollout Phase … not all of them of the Altruistic Nature (to be kind).

There can be a very good strategic arguments of not sharing economical attractive site locations depending on the particular business cycle and competitive climate of a given market. The value certain sites market potential could  justify to not give them up for sharing. Particular if competitor time-to-market in those highly attractive areas gets delayed. This said there is hardly any reason for not sharing rural sites where the Ugly (Cost) Tail of low or no profitable sites are situated. Being able to share such low-no-profitability sites simply allow operators to re-focus cash on areas where it really matters. Sharing allows services can be offered in rural and under-develop areas at the lowest cost possible. Particular in emerging markets rural areas, where a fairly large part of the population will be living, the cost of deploying and operating sites will be a lot more expensive than in urban areas. Combined with rural areas substantially lower population density it follows that sites will be a lot harder to make positively return on investment within their useful lifetime.

Total Cost of Ownership of rural sites are in many countries substantially higher than their urban equivalents. Low or No site profitability follows.

In general it can be shown that between 40% to 50% of mature operators sites generates less than 10% of the revenue and are substantially more expensive to deploy and operate than urban sites.

The ugly (cost) tail is a bit more “ugly” in mature western markets (i.e., 50+% of sites) than in emerging markets, as the customers in mature markets have higher coverage expectations in general.


(Source: Western European market. Similar Ugly-tail curves observed in many emerging markets as well although the 10% breakpoint tend to be close to 40%).

It is always recommend to analyze the most obvious strategic games that can be played out. Not only from your own perspective. More importantly, you need to have a comprehensive understanding of your competitors (and sharing partners) games and their most efficient path (which is not always synergetic or matching your own). Cost Structure Engineering should not only consider our own cost structure but also those of your competitors and partners.

Sharing is something that is very fundamental to the human nature. Sharing is on the fundamental level the common use of a given resource, tangible as well as intangible.

Sounds pretty nice! However, Sharing is rarely altruistic in nature i.e., lets be honest … why would you help a competitor to get stronger financially and have him spend his savings for customer acquisition … unless of course you achieve similar or preferably better benefits. It is a given that all sharing stakeholders should stand to benefit from the act of sharing. The more asymmetric perceived or tangible sharing benefits are the less stable will a sharing relationship be (or become over time if the benefit distribution should change significantly).

Recipe for a successful sharing partnership is that the sharing partners both have a perception of a deal that offers reasonable symmetric benefits.

It should be noted that perception of symmetric benefits does not mean per see that every saving or avoidance dollar of benefit is exactly the same for both partners. One stakeholder might get access to more coverage or capacity faster than in standalone. The other stakeholder might be able to more driven by budgetary concerns and sharing allows more extensive deployment than otherwise would have been possible within allocated budgets.

Historical most network sharing deals have focused on RAN Sharing, comprising radio access network (RAN) site locations, related passive infrastructure (e.g., such as tower, cabinets, etc..) and various degrees of active sharing. Recent technology development such as software definable network (SDN), virtualization concepts (e.g., Network Function Virtualization, NFV) have made sharing of core network and value-add service platforms interesting as well (or at least more feasible). Another financially interesting industry trend is to spin-off an operators tower assets to 3rd party Tower Management Companies (TMC). The TMC pays upfront a cash equivalent of the value of the passive tower infrastructure to the Mobile Network Operator (MNO). The MNO then lease (i.e., Opex) back the tower assets from the TMC. Such tower asset deals provide the MNO with upfront cash and the TMC a long-term lease income from the MNO. In my opinion such Tower deals tend to be driven by MNOs short-term cash needs without much regard for longer  term profitability and Ebitda (i.e., Revenue minus Opex) developments.

With ever increasing demand for more and more bandwidth feeding our customers mobile internet consumption, fiber optical infrastructures have become a must have. Legacy copper-based fixed transport networks can no longer support such bandwidth demands. Over the next 10 years all Telco’s will face massive investments into fiber-optic networks to sustain the ever growing demand for bandwidth. Sharing such investments should be obvious and straightforward. In this area we also are faced with the choice of passive (Dark Fiber itself) as well as active (i.e., DWDM) infrastructure sharing.


There are many consultants out there who evangelize network sharing as the only real cost reduction / saving measure left to the telecom industry. In Theory they are not wrong. The stories that will be told are almost too good to be true. Are you “desperate” for economical efficiency? You might then get very exited by the network sharing promise and forget that network sharing also has a cost side to it (i.e., usually forget and denial are fairly interchangeable here).

In my experience Network Sharing boils down to  the following 4 points:

  • Who to share with? (your equal, your better or your worse).
  • What to share? (sites, passives, active, frequencies, new sites, old sites, towers, rooftops, organization, ,…).
  • Where to share? (rural, sub-urban, urban, regional, all, etc..).
  • How to share? (“the legal stuff”).

In my more than 14 years of thinking about and working on Network Sharing I have come to the following heuristics of the pre-requisites a successful network sharing:

  • CEOs agree with & endorse Network Sharing.
  • Sharing Partners have similar perceived benefits (win-win feel).
  • Focus on creating a better network for less and with better time-to-market..
  • Both parties share a similar end-goal and have a similar strategic outlook.

While it seems obvious it is often forgotten that Network Sharing is a very-long term engagement (“for Life!”) and like in any other relationship (particular the JV kind) Do consider that a break-up can happen … so be prepared (i.e., “legal stuff”).

Compared to 14 – 15 years ago, Technology pretty much support Network Sharing in all its flavors and is no longer a real show-stopper for engaging with another operator to share network and ripe of (eventually) the financial benefits of such a relationship. References on the technical options for network sharing can be found in the 3GPP TR 3GPP TS 22.951 (“Service Aspects and Requirements for network sharing”) and 123.251 (“Network Sharing; Architecture and Functional Description”). Obviously, today 3GPP support for network sharing runs through most of the 3GPP technical requirements and specification documents.

Technology is not a show-stopper for Network Sharing. The Economics might be!


Before committing man power to a network sharing deal, there are a couple of pretty basic “litmus tests” to be done to see whether the economic savings being promised make sense.

First understand your own cost structure (i.e., Capex, Opex, Cash and Revenues) and in particular where Network Sharing will make an impact – positive as well as negative. I am more often that not, surprised how few Executives and Senior Managers really understand their own company’s cost structure. Thus they are not able to quickly spot un-realistic financial & operational promises made.

Seek answers to the following questions:

  1. What is the Total Technology Opex (Network & IT) share out of the Total Corporate Opex?
  2. What is the Total Network Opex out of Total Technology Opex?
  3. What is the Total Radio Access Network (RAN) Opex out of the Total Network Opex?
  4. Out of the Total RAN Opex how much relates to sites including Operations & Maintenance?

expectation management

In general, I would expect the following answers to the above questions based on many of mobile operator cost structure analysis across many different markets (from mature to very emerging, from Western Europe, Central Eastern & Southern Europe, to US and Asia-Pacific).

  1. Technology Opex is 20% to 25% of Total Corporate Opex defined as “Revenue-minus-Ebitda”(depends a little on degree of leased lines & diesel generator dependence).
  2. Network Opex should be between  70% to 80% of the Technology Opex.,
  3. RAN related Opex should be between 50% to 80% of the Network Opex. Of course here it is important to understand that not all of this Opex might be impacted by Network Sharing or at least the impact would depend on the Network Sharing model chosen (e.g., active versus passive).

Lets assume that a given RAN network sharing scenario provides a 35% saving on Total RAN Opex, that would be 35% (RAN Saving) x 60% (RAN Opex) x 75% (Network Opex) x 25% (Technology Opex) which yields a total network sharing saving of 4% on the Corporate Opex.

A saving on Opex obviously should translate into a proportional saving on Ebitda (i.e., Earnings before interest tax depreciation & amortization). The margin saving is given as follows

\frac{{{E_2} - {E_1}}}{{{E_1}}} = \frac{{1 - {m_1}}}{{{m_1}}}x(with E1 and E2 represents Ebitda before and after the relative Opex saving x, m1 is the margin before the Opex saving, assuming that Revenue remains unchanged after Opex saving has been realized).

From the above we see that when the margin is exactly 50% (i.e., fairly un-usual phenomenon for most mature markets), a saving in Opex corresponds directly to an identical relative saving in Ebitda. When the margin is below 50% the relative impact on Ebitda is higher than the relative saving on Opex. If your margin was 40% prior to a realized Opex saving of 5%, one would expect the margin (or Ebitda) saving to be 1.5x that saving or 7.5%.

In general I would expect up-to 35% Opex saving on relevant technology cost structure from network sharing on established networks. If much more saving is claimed, we should get skeptical of the analysis and certainly not take it on face value. It is not un-usual to see Network Sharing contributing as much as 20% saving (and avoidance on run-rate) on the overall Network Opex (ignoring IT Opex here!).

Why not 50% saving (or avoidance)? You may ask! But only once please!

After all we are taking 2 RAN networks and migrating them into 1 network … surely that should result in at 50% saving (i.e., always on relevant cost structure).

First of all, not all relevant (to cellular sites) cost structure is in general relevant to network sharing. Think here about energy consumption and transport solutions as the most obvious examples. Further, landlords are not likely to allow you to directly share existing site locations, and thus site lease cost with another operator without asking for an increased lease (i.e., 20% to 40% is not un-heard of). Existing lease contracts might need to be opened up to allow sharing, terms & conditions will likely need to be re-negotiated, etc.. in the end site lease savings are achievable but these will not translate into a 50% saving.

WARNING! 50% saving claims as a result of Network Sharing are not to be taken at face value!

Another interesting effect is that more shared sites will eventually result compared to the standalone number of sites. In other words, the shared network will have sites than either of the two networks standalone (and hopefully less than the combined amount of sites prior to sharing & consolidation). The reason for this is that the two sharing parties networks rarely are completely symmetric when it comes to coverage. Thus the shared network that will be somewhat bigger than compared to the standalone networks and thus safeguard the customer experience and hopefully the revenue in a post-merged network scenario. If the ultimate shared network has been planned & optimized properly, both parties customers will experience an increased network quality in terms of coverage and capacity (i.e., speed).

#SitesA , #SitesB < #SitesA+B < #SitesA + #SitesB

The Shared Network should always provide a better network customer experience than each standalone networks.

I have experienced Executives argue (usually post-deal obviously!) that it is not possible to remove sites, as any site removed will destroy customer experience. Let me be clear, If the shared network is planned & optimized according with best practices the shared network will deliver a substantial better network experience to the combined customer base than the respective standalone networks.

Lets dive deeper into the Technology Cost Structure. As the Figure below shows (i.e., typical for mature western markets) we have the following high level cost distribution for the Technology Opex

  1. 10% to 15% for Core Network
  2. 20% to 40% for IT & Platforms and finally
  3. 45% to 70% for RAN.

The RAN Opex for markets without energy distribution challenges, i.e., mature & reliable energy delivery grid) is split in (a) ca. 40% (i.e., of the RAN Opex) for Rental & Leasing which is clearly addressable by Network Sharing, (b) ca. 25% in Services including Maintenance & Repair of which at least the non-Telco part is easily addressable by Network Sharing, (c) ca. 15% Personnel Cost also addressable by Network Sharing, (d) 10% Leased Lines (typical backhaul connectivity) is less dependent on Network Sharing although bandwidth volume discounts might be achievable by sharing connectivity to a shared site and finally (e) Energy & other Opex costs would in general not be impacted substantially by Network Sharing. Note that for markets with a high share of diesel generators and fuel logistics, the share of Energy cost within the RAN Opex cost category will be substantially larger than depicted here.

It is important to note here that sharing of Managed Energy Provision, similar to Tower Company lease arrangement, might provide financial synergies. However, typically one would expect Capex Avoidance (i.e., by not buying power systems) on the account of an increased Energy Opex Cost (compared to standalone energy management) for the managed services. Obviously, if such a power managed service arrangement can be shared, there might be some synergies to be gained from such an arrangement. In my opinion this is particular interesting for markets with a high reliance of diesel generators and fuelling logistics.This said

Power sharing in mature markets with high electrification rates can offer synergies on energy via applicable volume discounts though would require shared metering (which might not always be particular well appreciated by power companies).technology cost distribution

Maybe as much as

80% of the total RAN Opex can be positively impacted (i.e., reduced) by network sharing.

Above cost structure illustration also explain why I rarely get very exited about sharing measures in Core Network Domain (i.e., spend too much time in the past to explain that while NG Core Network might save 50% of relevant cost it really was not very impressive in absolute terms and efforts was better spend on more substantial cost structure elements). Assume you can save 50% (which is a bit on the wild side today) on Core Network Opex (even Capex is in proportion to RAN fairly smallish). That 50% saving on Core translates into maybe maximum 5% of the Network Opex as opposed to RAN’s 15% – 20%. Sharing Core Network resources with another party does require substantially more overhead management and supervision than even fairly aggressive RAN sharing scenarios (with substantial active sharing).

This said, I believe that there are some internal efficiency measures to Telco Groups (with superior interconnection) and very interesting new business models out there that do provide core network & computing infrastructure as a service to Telco’s (and in principle allow multiple Telco’s to share the core network platforms and resources. My 2012 presentation on Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. illustrates how such business models might work out. The first describes in largely generic terms how virtualization (e.g., NFV) and cloud-based technologies could be exploited. The LTE-as-a-Service (could be UMTS-as-a-Service as well of course) is more operator specific. The verdict is still out there whether truly new business models can provide meaningful economics for customer networks and business. In the longer run, I am fairly convinced, that scale and expected massive improvements in connectivity in-countries and between-countries will make these business models economical interesting for many tier-2, tier-3 and Generation-Z businesses.




From a Network Sharing perspective Capex synergies or Capex avoidance are particular interesting at the beginning of a network rollout (i.e., Rollout Phase) as well as at the end of the Steady State where technology refreshment is required (i.e., the Modernization Phase).

Obviously, in a site deployment heavy scenario (e.g., start-ups) sharing the materials and construction cost of greenfield tower or rooftop (in as much as it can be shared) will dramatically lower the capital cost of deployment. In particular as you and your competitor(s) would likely want to cover pretty much the same places and thus sharing does become very compelling and a rational choice. Unless its more attractive to block your competitor from gaining access to interesting locations.

Irrespective, between 40% to 50% of an operators sites will only generate up-to 10% of the turnover. Those ugly-cost-tail sites will typically be in rural areas (including forests) and also on average be more costly to deploy and operate than sites in urban areas and along major roads.

Sharing 40% – 50% of sites, also known as the ugly-cost-tail sites, should really be a no brainer!

Depending on the market, the country particulars, and whether we look at emerging or mature markets there might be more or less Tower sites versus rooftops. Rooftops are less obvious passive sharing candidates, while Towers obviously are almost perfect passive sharing candidates provided the linked budget for the coverage can be maintained post-sharing. Active sharing does make rooftop sharing more interesting and might reduce the tower design specifications and thus optimize Capex further in a deployment scenario.

As operators faces RAN modernization pressures it can Capex-wise become very interesting to discuss active as well as passive sharing with a competitor in the same situation. There are joint-procurement benefits to be gained as well as site consolidation scenarios that will offer better long-term Opex trends. Particular T-Mobile and Hutchinson in the UK (and T-Mobile and Orange as well in UK and beyond) have championed this approach reporting very substantial sourcing Capex synergies by sharing procurements. Note network sharing and sharing sourcing in a modernization scenario does not force operators to engage in full active network sharing. However, it is a pre-requisite that there is an agreement on the infrastructure supplier(s).

Network Sharing triggered by modernization requirements is primarily interesting (again Capex wise) if part of electronics and ancillary can be shared (i.e., active sharing). Suppliers match is an obviously must for optimum benefits. Otherwise the economical benefits will be weighted towards Opex if a sizable amount of sites can be phased out as a result of site consolidation.


The above Figure provides an overview of the most interesting components of Network Sharing. It should be noted that Capex prevention is in particular relevant to (1) The Rollout Phase and (2) The Modernization Phase. Opex prevention is always applicable throughout the main 3 stages Network Sharing Attractiveness Cycles. In general the Regulatory Complexity tend to be higher for Active Sharing Scenarios and less problematic for Passive Sharing Scenarios. In general Regulatory Authorities would (or should) encourage & incentivize passive site sharing ensuring that an optimum site infrastructure (i.e., number of towers & rooftops) is being built out (in greenfield markets) or consolidated (in established / mature markets). Even today it is not un-usual to find several towers, each occupied with a single operator, next to each other or within hundred of meters distance.


One of the first things a responsible executive should ask when faced with the wonderful promises of network sharing synergies in form of Ebitda and cash improvements is

What does it cost me to network share?

The amount of re-structuring or termination cost that will be incurred before Network Sharing benefits can be realized will depend a lot on which part of the Network Sharing Cycle.

(1) The Rollout Phase in which case re-structuring cost is likely to be minimum as there is little or nothing to restructure. Further, also in this case write-off of existing investments and assets would likewise be very small or non-existent pending on how far into the rollout the business would be. What might complicate matters are whether sourcing contracts needs to be changed or cancelled and thus result in possible penalty costs. In any event being able to deploy together the network from the beginning does (in theory) result in the least deployment complexity and best deployment economics. However, getting to the point of agreeing to shared deployment (i.e., which also requires a reasonable common site grid) might be a long and bumpy road. Ultimately, launch timing will be critical to whether two operators can agree on all the bits and pieces in time not to endanger targeted launch.

Network Sharing in the Rollout Phase is characterized by

  • Little restructuring & termination cost expected.
  • High Capex avoidance potential.
  • High  Opex avoidance potential.
  • Little to no infrastructure write-offs.
  • Little to no risk of contract termination penalties.
  • “Normal” network deployment project (though can be messed up by too many cooks syndrome).
  • Best network potential.

    (2) The Steady State Phase, where a substantial part of the networks have been rollout out, tend to be the most complex and costly phase to engage in Network Sharing passive and of course active sharing. A substantial amount of site leases would need to be broken, terminated or re-structured to allow for network sharing. In all cases either penalties or lease increases are likely to result. Infrastructure supplier contracts, typically maintenance & operations agreements, might likewise be terminated or changed substantially. Same holds for leased transmission. Write-off can be very substantial in this phase as relative new sites might be terminated, new radio equipment might become redundant or phased-out, etc If one or both sharing partners are in this phase of the business & network cycle the chance of a network sharing agreement is low. However, if a substantial amount of both parties site locations will be used to enhance the resulting network and a substantial part of the active equipment will be re-used and contracts expanded then sharing tends to be going ahead. A good example of this is in the UK with Vodafone and O2 site sharing agreement with the aim to leapfrog number of sites to match that of EE (Orange + T-Mobile UK JV) for improved customer experience and remain competitive with the EE network.

    Network Sharing in the Steady State Phase is characterized by

  • Very high restructuring & termination cost expected.
  • None or little Capex synergies.
  • Substantial Opex savings potential.
  • Very high infrastructure write-offs.
  • Very high termination penalties incl. site lease termination.
  • Highly complex consolidation project.
  • Medium to long-term network quality & optimization issues.

    (3) Once operators approaches the Modernization Phase more aggressive network sharing scenarios can be considered as the including joint sourcing and infrastructure procurement (e.g., a la T-Mobile UK and Hutchinson in UK). At this stage typically the remainder of the site leases term will be lower and penalties due to lease termination as a result lower as well. Furthermore, at this point in time little (or at least substantially lower than in the steady state phase) residual value should remain in the active and also passive infrastructure. The Modernization Phase is a very opportune moment to consider network sharing, passive as well as active, resulting in both substantial Capex avoidance and of course very attractive Opex savings mitigating a stagnating or declining topline as well as de-risking future loss of profitability.

    Network Sharing in the Modernization Phase is characterized by

    • Relative moderate restructuring & termination cost expected.
    • High Capex avoidance potential.
    • Substantial Opex saving potential.
    • Little infrastructure write-offs.
    • Lower risk of contract termination penalties.
    • Manageable consolidation project.
    • Instant cell splits and cost-efficient provision of network capacity.
    • More aggressive network optimization –> better network.

    As a rule of thumb I usually recommend to estimate restructuring / termination cost as follows (i.e., if you don’t have the real terms & conditions of contracts by the hand);

    1. 1.5 to 3+ times the estimated Opex savings – use the higher multiple in the Steady State Phase and the Lower for Modernization Phase.
    2. Consolidation Capex will often be partly synergetic with Business-as-Usual (BaU) Capex and should not be fully considered (typically between 25% to 50% of consolidation Capex can be mapped to BaU Capex).
    3. Write-offs should be considered and will be the most pain-full to cope with in the Steady State Phase.


    A National Roaming agreement will save network investments and the resulting technology Opex. So in terms of avoiding technology cost that’s an easy one. Of course from a Profit & Loss (P&L) perspective I am replacing my technology Opex and Capex with wholesale cost somewhere else in my P&L. Whether National Roaming is attractive or not will depend a lot of anticipated traffic and of course the wholesale rate the hosting network will charge for the national roaming service. Hutchinson in UK (as well in other markets) had for many years a GSM national roaming agreement with Orange UK, that allowed its customers basic services outside its UMTS coverage footprint. In Austria for example, Hutchinson (i.e., 3 Austria) provide their customers with GSM national roaming services on T-Mobile Austria’s 2G network (i.e., where 3 Austria don’t cover with their own 3G) and T-Mobile Austria has 3G national roaming arrangement with Hutchinson in areas that they do not cover with 3G.

    In my opinion whether national roaming make sense or not really boils down to 3 major considerations for both parties:


    There are plenty of examples on National Roaming which in principle can provide similar benefits to infrastructure sharing by avoidance of Capex & Opex that is being replaced by the cost associated with the traffic on the hosting network.The Hosting MNO gets wholesale revenue from the national roaming traffic which the Host supports in low-traffic areas or on a under-utilized network. National roaming agreements or relationships tends to be of temporary nature.

    It should be noted that National Roaming is defined in an area were 1-Party The Host has network coverage (with excess capacity) and another operator (i.e., The Roamer or The Guest) has no network coverage but has a desire to offer its customers service in that particular area. In general only the host’s HPLMN is been broadcasted on the national roaming network. However, with Multi-Operator Core Network (MOCN) feature it is possible to present the national roamer with the experience of his own network provided the roamers terminal equipment supports MOCN (i.e., Release 8 & later terminal equipment will support this feature).

    In many Network Sharing scenarios both parties have existing and overlapping networks and would like to consolidate their networks to one shared network without loosing service quality. The reduction in site locations provide the economical benefits of network sharing. Throughout the shared network both operators will radiate  their respective HPLMNs and the shared network will be completely transparent to their respective customer bases.

    While having been part of several discussions to shut down one networks in geographical areas of a market and move customers to a host overlapping (or better) network via a national roaming agreement, I am not aware of mobile operators which have actually gone down this path.

    Regulatory and from a spectrum safeguard perspective it might be a better approach to commission both parties frequencies on the same network infrastructure and make use of for example the MOCN feature that allows full customer transparency (at least for Release 8 and later terminals).

    national_roaming _examples

    National Roaming is fully standardized and a well proven arrangement in many markets around the world. One does need to be a bit careful with how the national roaming areas are defined/implemented and also how customers move back and forth from a national roaming area (and technology) to home area (and technology). I have seen national roaming arrangements not being implemented because the dynamics was too complex to manage. The “cleaner” the national roaming area is the simpler does the on-off national roaming dynamics become. With “Clean” is mean keep the number of boundaries between own and national roaming network low, go for contiguous areas rather than many islands, avoid different technology coverage overlap (i.e., area with GSM coverage, it should avoided to do UMTS national roaming), etc.. Note you can engineer a “dirty” national roaming scenario of course. However, those tend to be fairly complex and customer experience management tends to be sub-optimal.

    Network Sharing and National Roaming are from a P&L perspective pretty similar in the efficiency and savings potentials. The biggest difference really is in the Usage Based cost item where a National Roaming would incur higher cost than compared to a Network Sharing arrangement.


    An Example: Operator contemplate 2 scenarios;

    1. Network Sharing in rural area addressing 500 sites.
    2. Terminate 500 sites in rural area and make use of National Roaming Agreement.

    What we are really interested in, is to understand when Network Sharing provides better economics than National Roaming and of course vice versa.

    National Roaming can be attractive for relative low traffic scenarios or in case were product of traffic units and national roaming unit cost remains manageable and lower than the Shared Network Cost.

    national roaming vs network sharing

    The above illustration ignores the write-off and termination charges that might result from terminating a given number of sites in a region and then migrate traffic to a national roaming network (note I have not seen any examples of such scenarios in my studies).

    The termination cost or restructuring cost, including write-off of existing telecom assets (i.e., radio nodes, passive site solutions, transmission, aggregation nodes, etc….) is likely to be a substantially financial burden to National Roaming Business Case in an area with existing telecom infrastructure. Certainly above and beyond that of a Network Sharing scenario where assets are being re-used and restructuring cost might be partially shared between the sharing partners.

    Obviously, if National Roaming is established in an area that has no network coverage, restructuring and termination cost is not an issue and Network TCO will clearly be avoided, Albeit the above economical logic and P&L trade-offs on cost still applies.

    National Roaming can be an interesting economical alternative, at least temporarily, to Network Sharing or establishing new coverage in an area with established network operators.

    However, National Roaming agreements are usually of temporary nature as establishing own coverage either standalone or via Network Sharing eventually will be a better economical and strategic choice than continuing with the national roaming agreement.


    There is a school of thought, within the Telecommunications Industry, that very much promotes the idea of relying on Tower Companies (Towerco) to provide and manage passive telecom site infrastructure.

    The mobile operator leases space from the Towerco on the tower (or in some instances a rooftop) for antennas, radio units and possible microwave dishes. Also the lease would include some real estate space around the tower site location for the telecom racks and ancillary equipment.

    In the last 10 years many operators have sold off their tower assets to Tower companies that then lease those back to the mobile operator.

    In most Towerco deals, Mobile Operators are trading off up-front cash for long-term lease commitments.

    With the danger of generalizing, Towerco deals made by operators in my opinion have a bit the nature and philosophy of “The little boy peeing in his trousers on a cold winter day, it will warm him for a short while, in the long run he will freeze much more after the act”. Let us also be clear that the business down the road will not care about a brilliant tower deal (done in the past) if it pressures their Ebitda and Site Lease cost.

    In general the Tower company will try (should be incented) to increase the tower tenancy (i.e., having more tenants per tower). Pending on the lease contract the Towerco might (should!) provide the mobile operator lease discount as more tenants are added to a given tower infrastructure.

    Towerco versus Network Sharing is obviously a Opex versus Capex trade-off. Anyway, lets look at a simple total-cost-of-ownership example that allows us to understand better when one strategy could be better than the other.towerco vs network sharing

    From the above very simple and high level per tower total-cost-of-ownership model its clear that a Towerco would have some challenges in matching the economics of the Shared Network. A Mobile Operator would most likely (in above example) be better of commencing on a simple tower sharing model (assuming a sharing partner is available and not engaging with another Towerco) rather than leasing towers from a Towerco. The above economics is ca. 600 US$ TCO per month (2-sharing scenario) compared to ca. 1,100 (2-tenant scenario). Actually, unless the Towerco is able to (a) increase occupancy beyond 2, (b) reduce its productions cost well below what the mobile operators would be (without sacrificing quality too much), and (c) at a sufficient low margin, it is difficult to see how a Towerco can provide a Tower solution at better economics than conventional network shared tower.

    This said it should also be clear that the devil will be in the details and there are various P&L and financial engineering options available to mobile operators and Towercos that will improve on the Towerco model. In terms of discounted cash flow and NPV analysis of the cash flows over the full useful life period the Network Sharing model (2-parties) and Towerco lease model with 2-tenants can be made fairly similar in terms of value. However, for 2-tenant versus 2-party sharing, the Ebitda tends to be in favor of network sharing.

    For the Mobile Network Operator (MNO) it is a question of committing Capital upfront versus an increased lease payment over a longer period of time. Obviously the cost of capital factors in here and the inherent business model risk. The inherent risk factors for the Towerco needs to be considered in its WACC (weighted average cost of capital) and of course the overall business model exposure to

    1. Operator business failure or consolidation.
    2. Future Network Sharing and subsequent lease termination.
    3. Tenant occupancy remains low.
    4. Contract penalties for Towerco non-performance, etc..

    Given the fairly large inherent risk (to Towerco business models) of operator consolidation in mature markets, with more than 3 mobile operators, there would be a “wicked” logic in trying to mitigate consolidation scenarios with costly breakaway clauses and higher margins.

    From all the above it should be evident that for mobile operators with considerable tower portfolios and also sharing ambitions, it is far better to (First) Consolidate & optimize their tower portfolios, ensuring minimum 2 tenants on each tower and then (Second) spin-off (when the cash is really needed) the optimized tower portfolio to a Towerco ensuring that the long-term lease is tenant & Ebitda optimized (as that really is going to be any mobile operations biggest longer term headache as markets starts to saturate).


    There should be little doubt that

    Network Sharing provides one of the biggest financial efficiency levers available to mobile network operator.

    Maybe apart from reducing market invest… but that is obviously not really a sustainable medium-long-term strategy.

    In aggressive network sharing scenarios Opex savings in the order of 35% is achievable as well as future Opex avoidance in the run-rate. Depending on the Network Sharing Scenario substantial Capex can be avoided by sharing the infrastructure built-out (i.e., The Rollout Phase) and likewise in the Modernization Phase. Both allows for very comprehensive sharing of both passive and active infrastructure and the associated capital expenses.

    Both National Roaming and Sharing via Towerco can be interesting concepts and if engineered well (particular financially) can provide similar benefits as sharing (active as well as passive, respectively). Particular in cash constrained scenarios (or where operators see an extraordinary business risk and want to minimize cash exposure) both options can be attractive. Long-term National Roaming is particular attractive in areas where an operator have no coverage and has little strategic importance. In case an area is strategically important, national roaming can act as a time-bridge until presence has been secure possibly via Network Sharing (if competitor is willing).

    Sharing via Towerco can also be an option when two parties are having trust issues. Having a 3rd party facilitating the sharing is then an option.

    In my opinion National Roaming & Sharing via Towerco rarely as Ebitda efficient as conventional Network Sharing.

    Finally! Why should you stay away from Network Sharing?

    This question is important to answer as well as why you should (which always seems initially the easiest). Either to indeed NOT to go down the path of network sharing or at the very least ensure that point of concerns and possible blocking points have been though roughly considered and checked of.

    So here comes some of my favorites … too many of those below you are not terrible likely to be successful in this endeavor:



    I would like to thank many colleagues for support and Network Sharing discussions over the past 13 years. However, in particular I owe a lot to David Haszeldine (Deutsche Telekom) for his insights and thoughts. David has been my true brother-in-arms throughout my Deutsche Telekom years and on our many Network Sharing experiences we have had around the world. I have had many & great discussions with David on the ins-and-outs of Network Sharing … Not sure we cracked it all? … but pretty sure we are at the forefront of understanding what Network Sharing can be and also what it most definitely cannot do for a Mobile Operator. Of course similar to all the people who have left comments on my public presentations and gotten in contact with me on this very exiting and by no way near exhausted topic of how to share networks.

    The term the “Ugly Tail” as referring to rural and low-profitability sites present in all networks should really be attributed to Fergal Kelly (now CTO of Vodafone Ireland) from a meeting quiet a few years ago. The term is too good not to borrow … Thanks Fergal!

    This story is PART I and as such it obviously would indicate that another Part is on the way Winking smilePART II“Network Sharing – That was then, this is now” will be on the many projects I have worked on in my professional career and lessons learned (all available in the public domain of course). Here obviously providing a comparison with the original ambition level and plans with the reality is going to be cool (and in some instances painful as well). PART III“The Tools” will describe the arsenal of tools and models that I have developed over the last 13 years and used extensively on many projects.

  • The Economics of the Thousand Times Challenge: Spectrum, Efficiency and Small Cells

    By now the biggest challenge of the “1,000x challenge” is to read yet another story about the “1,000x challenge”.

    This said, Qualcomm has made many beautiful presentations on The Challenge. It leaves the reader with an impression that it is much less of a real challenge, as there is a solution for everything and then some.

    So bear with me while we take a look at the Economics and in particular the Economical Boundaries around the Thousand Times “Challenge” of providing (1) More spectrum, (2) Better efficiency and last but not least (3) Many more Small Cells.


    While (almost) every technical challenge is solvable by clever engineering (i.e., something Qualcomm obviously have in abundance), it is not following naturally that such solutions are also feasible within the economical framework imposed by real world economics. At the very least, any technical solution should also be reasonable within the world of economics (and of course within a practical time-frame) or it becomes a clever solution but irrelevant to a real world business.

    A  Business will (maybe should is more in line with reality) care about customer happiness. However a business needs to do that within healthy financial boundaries of margin, cash and shareholder value. Not only should the customer be happy, but the happiness should extend to investors and shareholders that have trusted the Business with their livelihood.

    While technically, and almost mathematically, it follows that massive network densification would be required in the next 10 years IF WE KEEP FEEDING CUSTOMER DEMAND it might not be very economical to do so or at the very least such densification only make sense within a reasonable financial envelope.

    Its obvious that massive network densification, by means of macro-cellular expansion, is unrealistic, impractically as well as uneconomically. Thus Small Cell concepts including WiFi has been brought to the Telecoms Scene as an alternative and credible solution. While Small Cells are much more practical, the question whether they addresses sufficiently the economical boundaries, the Telecommunications Industry is facing, remains pretty much unanswered.


    The Thousand Times Challenge, as it has been PR’ed by Qualcomm, states that the cellular capacity required in 2020 will be at least 1,000 times that of “today”. Actually, the 1,000 times challenge is referenced to the cellular demand & supply in 2010, so doing the math

    the 1,000x might “only” be a 100 times challenge between now and 2020 in the world of Qualcomm’s and alike. Not that it matters! … We still talk about the same demand, just referenced to a later (and maybe less “sexy” year).

    In my previous Blogs, I have accounted for the dubious affair (and non-nonsensical discussion) of over-emphasizing cellular data growth rates (see “The Thousand Times Challenge: The answer to everything about mobile data”) as well as the much more intelligent discussion about how the Mobile Industry provides for more cellular data capacity starting with the existing mobile networks (see “The Thousand Time Challenge: How to provide cellular data capacity?”).

    As it turns out  Cellular Network Capacity C can be described by 3 major components; (1) available bandwidth B, (2) (effective) spectral efficiency E and (3) number of cells deployed N.

    The SUPPLIED NETWORK CAPACITY in Mbps (i.e., C) is equal to  the AMOUNT OF SPECTRUM, i.e., available bandwidth, in MHz (i..e, B) multiplied with the SPECTRAL EFFICIENCY PER CELL in Mbps/MHz (i.e., E) multiplied by the NUMBER OF CELLS (i.e., N). For more details on how and when to apply the Cellular Network Capacity Equation read my previous Blog on “How to provide Cellular Data Capacity?”).

    SK Telekom (SK Telekom’s presentation at the 3GPP workshop on “Future Radio in 3GPP” is worth a careful study) , Mallinson (@WiseHarbor) and Qualcomm (@Qualcomm_tech, and many others as of late) have used the above capacity equation to impose a Target amount of cellular network capacity a mobile network should be able to supply by 2020: Realistic or Not, this target comes to a 1,000 times the supplied capacity level in 2010 (i.e., I assume that 2010 – 2020 sounds nicer than 2012 – 2022 … although the later would have been a lot more logical to aim for if one really would like to look at 10 years … of course that might not give 1,000 times which might ruin the marketing message?).

    So we have the following 2020 Cellular Network Capacity Challenge:

    Thus a cellular network in 2020 should have 3 times more spectral bandwidth B available (that’s fairly easy!), 6 times higher spectral efficiency E (so so … but not impossible, particular compared with 2010) and 56 times higher cell site density N (this one might  be a “real killer challenge” in more than one way), compared to 2010!.

    Personally I would not get too hanged up about whether its 3 x 6 x 56 or 6 x 3 x 56 or some other “multiplicators” resulting in a 1,000 times gain (though some combinations might be a lot more feasible than others!)

    Obviously we do NOT need a lot of insights to see that the 1,000x challenge is a

    Rally call for Small & then Smaller Cell Deployment!

    Also we do not need to be particular visionary (or have visited a Dutch Coffee Shop) to predict that by 2020 (aka The Future) compared to today (i.e., October 2012)?

    Data demand from mobile devices will be a lot higher in 2020!

    Cellular Networks have to (and will!) supply a lot more data capacity in 2020!

    Footnote: the observant reader will have seen that I am not making the claim that there will be hugely more data traffic on the cellular network in comparison to today. The WiFi path might (and most likely will) take a lot of the traffic growth away from the cellular network.


    how economical will this journey be for the Mobile Network Operator?


    Mobile Network Operators (MNOs) will not have the luxury of getting the Cellular Data Supply and Demand Equation Wrong.

    The MNO will need to balance network investments with pricing strategies, churn & customer experience management as well as overall profitability and corporate financial well being:

    Growth, if not manage, will lead to capacity & cash crunch and destruction of share holder value!

    So for the Thousand Times Challenge, we need to look at the Total Cost of Ownership (TCO) or Total Investment required to get to a cellular network with 1,000 times more network capacity than today. We need to look at:

    Investment I(B) in additional bandwidth B, which would include (a) the price of spectral re-farming (i.e., re-purposing legacy spectrum to a new and more efficient technology), (b) technology migration (e.g., moving customers off 2G and onto 3G or LTE or both) and (c) possible acquisition of new spectrum (i..e, via auction, beauty contests, or M&As).

    Improving a cellular networks spectral efficiency I(E) is also likely to result in additional investments. In order to get an improved effective spectral efficiency, an operator would be required to (a) modernize its infrastructure, (b) invest into better antenna technologies, and (c) ensure that customer migration from older spectral in-efficient technologies into more spectral efficient technologies occurs at an appropriate pace.

    Last but NOT Least the investment in cell density I(N):

    Needing 56 times additional cell density is most likely NOT going to be FREE,

    even with clever small cell deployment strategies.

    Though I am pretty sure that some will make a very positive business case, out there in the Operator space, (note: the difference between Pest & Cholera might come out in favor of Cholera … though we would rather avoid both of them) comparing a macro-cellular expansion to Small Cell deployment, avoiding massive churn in case of outrageous cell congestion, rather than focusing on managing growth before such an event would occur.

    The Real “1,000x” Challenge will be Economical in nature and will relate to the following considerations:

    tco 2020

    In other words:

    Mobile Networks required to supply a 1,000 times present day cellular capacity are also required to provide that capacity gain at substantially less ABSOLUTE Total Cost of Ownership.

    I emphasize the ABSOLUTE aspects of the Total Cost of Ownership (TCO), as I have too many times seen our Mobile Industry providing financial benefits in relative terms (i.e., relative to a given quality improvement) and then fail to mention that in absolute cost the industry will incur increased Opex (compared to pre-improvement situation). Thus a margin decline (i.e., unless proportional revenue is gained … and how likely is that?) as well as negative cash impact due to increased investments to gain the improvements (i.e., again assuming that proportional revenue gain remains wishful thinking).

    Never Trust relative financial improvements! Absolutes don’t Lie!


    Spectrum economics can be captured by three major themes: (A) ACQUISITION, (B) RETENTION and (C) PERFECTION. These 3 major themes should be well considered in any credible business plan: Short, Medium and Long-term.

    It is fairly clear that there will not be a lot new lower frequency (defined here as <2.5GHz) spectrum available in the next 10+ years (unless we get a real breakthrough in white-space). The biggest relative increase in cellular bandwidth dedicated to mobile data services will come from re-purposing (i.e., perfecting) existing legacy spectrum (i.e., by re-farming). Acquisition of some new bandwidth in the low frequency range (<800MHz), which per definition will not be a lot of bandwidth and will take time to become available. There are opportunities in the very high frequency range (>3GHz) which contains a lot of bandwidth. However this is only interesting for Small Cell and Femto Cell like deployments (feeding frenzy for small cells!).

    As many European Countries re-auction existing legacy spectrum after the set expiration period (typical 10 -15 years), it is paramount for a mobile operator to retain as much as possible of its existing legacy spectrum. Not only is current traffic tied up in the legacy bands, but future growth of mobile data will critical depend on its availability. Retention of existing spectrum position should be a very important element of an Operators  business plan and strategy.

    Most real-world mobile network operators that I have looked at can expect by acquisition & perfection to gain between 3 to 8 times spectral bandwidth for cellular data compared to today’s situation.

    For example, a typical Western European MNO have

    1. Max. 2x10MHz @ 900MHz primarily used for GSM. Though some operators are having UMTS 900 in operation or plans to re-farm to UMTS pending regulatory approval.
    2. 2×20 MHz @ 1800MHz, though here the variation tend to be fairly large in the MNO spectrum landscape, i.e., between 2x30MHz down-to 2x5MHz. Today this is exclusively in use for GSM. This is going to be a key LTE band in Europe and already supported in iPhone 5 for LTE.
    3. 2×10 – 15 MHz @ 2100MHz is the main 3G-band (UMTS/HSPA+) in Europe and is expected to remain so for at least the next 10 years.
    4. 2×10 @ 800 MHz per operator and typically distributed across 3 operator and dedicated to LTE. In countries with more than 3 operators typically some MNOs will have no position in this band.
    5. 40 MHz @ 2.6 GHz per operator and dedicated to LTE (FDD and/or TDD). From a coverage perspective this spectrum would in general be earmarked for capacity enhancements rather than coverage.

    Note that most European mobile operators did not have 800MHz and/or 2.6GHz in their spectrum portfolios prior to 2011. The above list has been visualized in the Figure below (though only for FDD and showing the single side of the frequency duplex).


    The 700MHz will eventually become available in Europe (already in use for LTE in USA via AT&T and VRZ) for LTE advanced. Though the time frame for 700MHz cellular deployment in Europe is still expected take maybe up to 8 years (or more) to get it fully cleared and perfected.

    Today (as of 2012) a typical European MNO would have approximately (a) 60 MHz (i.e., DL+UL) for GSM, (b) 20 – 30 MHz for UMTS and (c) between 40MHz – 60MHz for LTE (note that in 2010 this would have been 0MHz for most operators!). By 2020 it would be fair to assume that same MNO could have (d) 40 – 50 MHz for UMTS/HSPA+ and (e) 80MHz – 100MHz for LTE. Of course it is likely that mobile operators still would have a thin GSM layer to support roaming traffic and extreme laggards (this is however likely to be a shared resource among several operators). If by 2020 10MHz to 20MHz would be required to support voice capacity, then the MNO would have at least 100MHz and up-to 130MHz for data.

    Note if we Fast-Backward to 2010, assume that no 2.6GHz or 800MHz auction had happened and that only 2×10 – 15 MHz @ 2.1GHz provided for cellular data capacity, then we easily get a factor 3 to 5 boost in spectral capacity for data over the period. This just to illustrate the meaningless of relativizing the challenge of providing network capacity.

    So what’s the economical aspects of spectrum? Well show me the money!


    1. needs to be Acquired (including re-acquired = Retention) via (a) Auction, (b) Beauty contest or (c) Private transaction if allowed by the regulatory authorities (i.e., spectrum trading); Usually spectrum (in Europe at least) will be time-limited right-to-use! (e.g., 10 – 15 years) => Capital investments to (re)purchase spectrum.
    2. might need to be Perfected & Re-farmed to another more spectral efficient technology => new infrastructure investments & customer migration cost (incl. acquisition, retention & churn).
    3. new deployment with coverage & service obligations => new capital investments and associated operational cost.
    4. demand could result in joint ventures or mergers to acquire sufficient spectrum for growth.
    5. often has a re-occurring usage fee associate with its deployment => Operational expense burden.

    First 3 bullet points can be attributed mainly to Capital expenditures and point 5. would typically be an Operational expense. As we have seen in US with the failed AT&T – T-Mobile US merger, bullet point 4. can result in very high cost of spectrum acquisition. Though usually a merger brings with it many beneficial synergies, other than spectrum, that justifies such a merger.


    Above Figure provides a historical view on spectrum pricing in US$ per MHz-pop. As we can see, not all spectrum have been borne equal and depending on timing of acquisition, premium might have been paid for some spectrum (e.g., Western European UMTS hyper pricing of 2000 – 2001).

    Some general spectrum acquisition heuristics can be derived by above historical overview (see my presentation “Techno-Economical Aspects of Mobile Broadband from 800MHz to 2.6GHz” on @slideshare for more in depth analysis).


    Most of the operator cost associated with Spectrum Acquisition, Spectrum Retention and Spectrum Perfection should be more or less included in a Mobile Network Operators Business Plans. Though the demand for more spectrum can be accelerated (1) in highly competitive markets, (2) spectrum starved operations, and/or (3) if customer demand is being poorly managed within the spectral resources available to the MNO.

    WiFi, or in general any open radio-access technology operating in ISM bands (i.e., freely available frequency bands such as 2.4GHz, 5.8GHz), can be a source of mitigating costly controlled-spectrum resources by stimulating higher usage of such open-technologies and open-bands.

    The cash prevention or cash optimization from open-access technologies and frequency bands should not be under-estimated or forgotten. Even if such open-access deployment models does not make standalone economical sense, is likely to make good sense to use as an integral part for the Next Generation Mobile Data Network perfecting & optimizing open- & controlled radio-access technologies.

    The Economics of Spectrum Acquisition, Spectrum Retention & Spectrum Perfection is of such tremendous benefits that it should be on any Operators business plans: short, medium and long-term.


    The relative gain in spectral efficiency (as well as other radio performance metrics) with new 3GPP releases has been amazing between R99 and recent HSDPA releases. Lots of progress have been booked on the account of increased receiver and antenna sophistication.


    If we compare HSDPA 3.6Mbps (see above Figure) with the first Release of LTE, the spectral efficiency has been improved with a factor 4. Combined with more available bandwidth for LTE, provides an even larger relative boost of supplied bandwidth for increased capacity and customer quality. Do note above relative representation of spectral efficiency gain largely takes away the usual (almost religious) discussions of what is the right spectral efficiency and at what load. The effective (what that may be in your network) spectral efficiency gain moving from one radio-access release or generation to the next would be represented by the above Figure.

    Theoretically this is all great! However,

    Having the radio-access infrastructure supporting the most spectral efficient technology is the easy part (i.e., thousands of radio nodes), getting your customer base migrated to the most spectral efficient technology is where the challenge starts (i.e., millions of devices).

    In other words, to get maximum benefits of a given 3GPP Release gains, an operator needs to migrate his customer-base terminal equipment to that more Efficient Release. This will take time and might be costly, particular if accelerated. Irrespective, migrating a customer base from radio-access A (e.g., GSM) to radio-access B (e.g., LTE), will take time and adhere to normal market dynamics of churn, retention, replacement factors, and gross-adds. The migration to a better radio-access technology can be stimulated by above-market-average acquisition & retention investments and higher-than-market-average terminal equipment subsidies. In the end competitors market reactions to your market actions, will influence the migration time scale very substantially (this is typically under-estimate as competitive driving forces are ignored in most analysis of this problem).

    The typical radio-access network modernization cycle has so-far been around 5 years. Modernization is mainly driven by hardware obsolescence and need for more capacity per unit area than older (first & second) generation equipment could provide. The most recent and ongoing modernization cycle combines the need for LTE introduction with 2G and possibly 3G modernization. In some instances retiring relative modern 3G equipment on the expense of getting the latest multi-mode, so-called Single-RAN equipment, deployed, has been assessed to be worth the financial cost of write-off.  This new cycle of infrastructure improvements will in relative terms far exceed past upgrades. Software Definable Radios (SDR) with multi-mode (i.e., 2G, 3G, LTE) capabilities are being deployed in one integrated hardware platform, instead of the older generations that were separated with the associated floor space penalty and operational complexity. In theory only Software Maintenance & simple HW upgrades (i.e., CPU, memory, etc..) would be required to migrate from one radio-access technology to another. Have we seen the last HW modernization cycle? … I doubt it very much! (i.e., we still have Cloud and Virtualization concepts going out to the radio node blurring out the need for own core network).

    Multi-mode SDRs should in principle provide a more graceful software-dominated radio-evolution to increasingly more efficient radio access; as cellular networks and customers migrate from HSPA to HSPA+ to LTE and to LTE-advanced. However, in order to enable those spectral-efficient superior radio-access technologies, a Mobile Network Operator will have to follow through with high investments (or incur high incremental operational cost) into vastly improved backhaul-solutions and new antenna capabilities than the past access technologies required.

    Whilst the radio access network infrastructure has gotten a lot more efficient from a cash perspective, the peripheral supporting parts (i.e., antenna, backhaul, etc..) has gotten a lot more costly in absolute terms (irrespective of relative cost per Byte might be perfectly OKAY).

    Thus most of the economics of spectral efficiency can and will be captured within the modernization cycles and new software releases without much ado. However, backhaul and antenna technology investments and increased operational cost is likely to burden cash in the peak of new equipment (including modernization) deployment. Margin pressure is therefor likely if the Opex of supporting the increased performance is not well managed.

    To recapture the most important issues of Spectrum Efficiency Economics:

    • network infrastructure upgrades, from a hardware as well as software perspective, are required => capital investments, though typically result in better Operational cost.
    • optimal customer migration to better and more efficient radio-access technologies => market invest and terminal subsidies.

    Boosting spectrum much beyond 6 times today’s mobile data dedicated spectrum position is unlikely to happen within a foreseeable time frame. It is also unlikely to happen in bands that would be very interesting for both providing both excellent depth of coverage and at the same time depth of capacity (i.e., lower frequency bands with lots of bandwidth available). Spectral efficiency will improve with both next generation HSPA+ as well as with LTE and its evolutionary path. However, depending on how we count the relative improvement, it is not going to be sufficient to substantially boost capacity and performance to the level a “1,000 times challenge” would require.

    This brings us to the topic of vastly increased cell site density and of course Small Cell Economics.


    It is fairly clear that there will not be a lot new spectrum available in the next 10+ years. The relative increase in cellular bandwidth will come from re-purposing & perfecting existing legacy spectrum (i.e., by re-farming) and acquiring some new bandwidth in the low frequency range (<800MHz) which per definition is not going to provide a lot of bandwidth.  The very high-frequency range (>3GHz) will contain a lot of bandwidth, but is only interesting for Small Cell and Femto-cell like deployments (feeding frenzy for Small Cells).

    Financially Mobile Operators in mature markets, such as Western Europe, will be lucky to keep their earning and margins stable over the next 8 – 10 years. Mobile revenues are likely to stagnate and possible even decline. Opex pressure will continue to increase (e.g., just simply from inflationary pressures alone). MNOs are unlikely to increase cell site density, if it leads to incremental cost & cash pressure that cannot be recovered by proportional Topline increases. Therefor it should be clear that adding many more cell sites (being it Macro, Pico, Nano or Femto) to meet increasing (often un-managed & unprofitable) cellular demand is economically unwise and unlikely to happen unless followed by Topline benefits.

    Increasing cell density dramatically (i.e., 56 times is dramatic!) to meet cellular data demand will only happen if it can be done with little incremental cost & cash pressure.

    I have no doubt that distributing mobile data traffic over more and smaller nodes (i.e., decrease traffic per node) and utilize open-access technologies to manage data traffic loads are likely to mitigate some of the cash and margin pressure from supporting the higher performance radio-access technologies.

    So let me emphasize that there will always be situations and geographical localized areas where cell site density will be increased disregarding the economics, in order to increase urgent capacity needs or to provide specialized-coverage needs. If an operator has substantially less spectral overhead (e.g., AT&T) than a competitor (e.g., T-Mobile US), the spectrum-starved operator might decide to densify with Small Cells and/or Distributed Antenna Systems (DAS) to be able to continue providing a competitive level of service (e.g., AT&T’s situation in many of its top markets). Such a spectrum starved operator might even have to rely on massive WiFi deployments to continue to provide a decent level of customer service in extreme hot traffic zones (e.g., Times Square in NYC) and remain competitive as well as having a credible future growth story to tell shareholders.

    Spectrum-starved mobile operators will move faster and more aggressively to Small Cell Network solutions including advanced (and not-so-advanced) WiFi solutions. This fast learning-curve might in the longer term make up for a poorer spectrum position.

    In the following I will consider Small Cells in the widest sense, including solutions based both on controlled frequency spectrum (e.g., HSPA+, LTE bands) as well in the ISM frequency bands (i.e., 2.4GHz and 5.8GHz). The differences between the various Small Cell options will in general translate into more or less cells due to radio-access link-budget differences.

    As I have been involved in many projects over the last couple of years looking at WiFi & Small Cell substitution for macro-cellular coverage, I would like to make clear that in my opinion:

    A Small Cells Network is not a good technical (or economical viable) solution for substituting macro-cellular coverage for a mobile network operator.

    However, Small Cells however are Great for

    • Specialized coverage solutions difficult to reach & capture with standard macro-cellular means.
    • Localized capacity addition in hot traffic zones.
    • Coverage & capacity underlay when macro-cellular cell split options have been exhausted.

    The last point in particular becomes important when mobile traffic exceeds the means for macro-cellular expansion possibilities, i.e., typically urban & dense-urban macro-cellular ranges below 200 meters and in some instances maybe below 500 meters pending on the radio-access choice of the Small Cell solution.

    Interference concerns will limit the transmit power and coverage range. However our focus are small localized and tailor-made coverage-capacity solutions, not a substituting macro-cellular coverage, range limitation is of lesser concern.

    For great accounts of Small Cell network designs please check out Iris Barcia (@IBTwi) & Simon Chapman (@simonchapman) both from Keima Wireless. I recommend the very insightful presentation from Iris “Radio Challenges and Opportunities for Large Scale Small Cell Deployments” which you can find at “3G & 4G Wireless Blog” by Zahid Ghadialy (@zahidtg, a solid telecom knowledge source for our Industry).

    When considering small cell deployment it makes good sense to understand the traffic behavior of your customer base. The Figure below illustrates a typical daily data and voice traffic profile across a (mature) cellular network:


    • up-to 80% of cellular data traffic happens either at home or at work.+

    Currently there is an important trend, indicating that the evening cellular-data peak is disappearing coinciding with the WiFi-peak usage taking over the previous cellular peak hour.

    A great source of WiFi behavioral data, as it relates to Smartphone usage, you will find in Thomas Wehmeier’s (Principal Analyst, Informa: @Twehmeier) two pivotal white papers on  “Understanding Today’s Smatphone User” Part I and Part II.

    The above daily cellular-traffic profile combined with the below Figure on cellular-data usage per customer distributed across network cells


    shows us something important when it comes to small cells:

    • Most cellular data traffic (per user) is limited to very few cells.
    • 80% (50%) of the cellular data traffic (per user) is limited to 3 (1) main cells.
    • The higher the cellular data usage (per user) the fewer cells are being used.

    It is not only important to understand how data traffic (on a per user) behaves across the cellular network. It is likewise very important to understand how the cellular-data traffic multiplex or aggregate across the cells in the mobile network.

    We find in most Western European Mature 3G networks the following trend:


    • 20% of the 3G Cells carries 60+% of the 3G data traffic.
    • 50% of the 3G Cells carriers 95% or more of the 3G data traffic.

    Thus relative few cells carries the bulk of the cellular data traffic. Not surprising really as this trend was even more skewed for GSM voice.

    The above trends are all good news for Small Cell deployment. It provides confidence that small cells can be effective means to taking traffic away from macro-cellular areas, where there is no longer an option for conventional capacity expansions (i.e., sectorization, additional carrier or conventional cell splits).

    For the Mobile Network Operator, Small Cell Economics is a Total Cost of Ownership exercise comparing Small Cell Network Deployment  to other means of adding capacity to the existing mobile network.

    The Small Cell Network needs (at least) to be compared to the following alternatives;

    1. Greenfield Macro-cellular solutions (assuming this is feasible).
    2. Overlay (co-locate) on existing network grid.
    3. Sectorization of an existing site solution (i.e., moving from 3 sectors to 3 + n on same site).

    Obviously, in the “extreme” cellular-demand limit where non of the above conventional means of providing additional cellular capacity are feasible, Small Cell deployment is the only alternative (besides doing nothing and letting the customer suffer). Irrespective we still need to understand how the economics will work out, as there might be instances where the most reasonable strategy is to let your customer “suffer” best-effort services. This would in particular be the case if there is no real competitive and incremental Topline incentive by adding more capacity.


    Competitive circumstances could force some spectrum-starved operators to deploy small cells irrespective of it being financially unfavorable to do so.

    Lets begin with the cost structure of a macro-cellular 3G Greenfield Rooftop Site Solution. We take the relevant cost structure of a configuration that we would be most likely to encounter in a Hot Traffic Zone / Metropolitan high-population density area which also is likely to be a candidate area for Small Cell deployment. The Figure below shows the Total Cost of Ownership, broken down in Annualized Capex and Annual Opex, for a Metropolitan 3G macro-cellular rooftop solution:


    Note 1: The annualized Capex has been estimated assuming 5 years for RAN Infra, Backaul & Core, and 10 years for Build. It is further assumed that the site is supported by leased-fiber backhaul. Opex is the annual operational expense for maintaining the site solution.

    Note 2: Operations Opex category covers Maintenance, Field-Services, Staff cost for Ops, Planning & optimization. The RAN infra Capex category covers: electronics, aggregation, antenna, cabling, installation & commissioning, etc..

    Note 3: The above illustrated cost structure reflects what one should expect from a typical European operation. North American or APAC operators will have different cost distributions. Though it is not expected to change conclusions substantially (just redo the math).

    When we discuss Small Cell deployment, particular as it relates to WiFi-based small cell deployment, with Infrastructure Suppliers as well as Chip Manufacturers you will get the impression that Small Cell deployment is Almost Free of Capex and Opex; i.e., hardly any build cost, free backhaul and extremely cheap infrastructure supported by no site rental, little maintenance and ultra-low energy consumption.

    Obviously if Small Cells cost almost nothing, increasing cell site density with 56 times or more becomes very interesting economics … Unfortunately such ideas are wishful thinking.

    For Small Cells not to substantially pressure margins and cash, Small Cell Cost Scaling needs to be very aggressive. If we talk about a 56x increase in cell site density the incremental total cost of ownership should at least be 56 times better than to deploy a macro-cellular expansion. Though let’s not fool ourselves!

    No mobile operator would densify their macro cellular network 56 times if absolute cost would proportionally increase!

    No Mobile operator would upsize their cellular network in any way unless it is at least margin, cost & cash neutral.

    (I have no doubt that out there some are making relative business cases for small cells comparing an equivalent macro-cellular expansion versus deploying Small Cells and coming up with great cases … This would be silly of course, not that this have ever prevented such cases to be made and presented to Boards and CxOs).

    The most problematic cost areas from a scaling perspective (relative to a macro-cellular Greenfield Site) are (a) Site Rental (lamp posts, shopping malls,), (b) Backhaul Cost (if relying on Cable, xDSL or Fiber connectivity), (c) Operational Cost (complexity in numbers, safety & security) and (d) Site Build Cost (legal requirements, safety & security,..).

    In most realistic cases (I have seen) we will find a 1:12 to 1:20 Total Cost of Ownership difference between a Small Cell unit cost and that of a Macro-Cellular Rooftop’s unit cost. While unit Capex can be reduced very substantially, the Operational Expense scaling is a lot harder to get down to the level required for very extensive Small Cell deployments.


    For a typical metropolitan rooftop (in Western Europe) we have the annualized capital expense (Capex) of ca. 15,000 Euro and operational expenses (Opex) in the order of 30,000 Euro per annum. The site-related Opex distribution would look something like this;

    • Macro-cellular Rooftop 3G Site Unit Annual Opex:
    • Site lease would be ca. 10,500EUR.
    • Backhaul would be ca. 9,000EUR.
    • Energy would be ca. 3,000EUR.
    • Operations would be ca. 7,500EUR.
    • i.e., total unit Opex of 30,000EUR (for average major metropolitan area)

    Assuming that all cost categories could be scaled back with a factor 56 (note: very big assumption that all cost elements can be scaled back with same factor!)

    • Target Unit Annual Opex cost for a Small Cell:
    • Site lease should be less than 200EUR (lamp post leases substantially higher)
    • Backhaul should be  less than 150EUR (doable though not for carrier grade QoS).
    • Energy should be less than 50EUR (very challenging for todays electronics)
    • Operations should be less than 150EUR (ca. 1 hour FTE per year … challenging).
    • Annual unit Opex should be less than 550EUR (not very likely to be realizable).

    Similar for the Small Cell unit Capital expense (Capex) would need to be done for less than 270EUR to be fully scalable with a macro-cellular rooftop (i.e., based on 56 times scaling).

    • Target Unit Annualized Capex cost for a Small Cell:
    • RAN Infra should be less than 100EUR (Simple WiFi maybe doable, Cellular challenging)
    • Backhaul would be less than 50EUR (simple router/switch/microwave maybe doable).
    • Build would be less than 100EUR (very challenging even to cover labor).
    • Core would be less than 20EUR (doable at scale).
    • Annualized Capex should be less than 270EUR (very challenging to meet this target)
    • Note: annualization factor: 5 years for all including Build.

    So we have a Total Cost of Ownership TARGET for a Small Cell of ca. 800EUR

    Inspecting the various capital as well as operational expense categories illustrates the huge challenge to be TCO comparable to a macro-cellular urban/dense-urban 3G-site configuration.

    Massive Small Cell Deployment needs to be almost without incremental cost to the Mobile Network Operator to be a reasonable scenario for the 1,000 times challenge.

    Most the analysis I have seen, as well as carried out myself, on real cost structure and aggressive pricing & solution designs shows that the if the Small Cell Network can be kept between 12 to 20 Cells (or Nodes) the TCO compares favorably to (i.e., beating) an equivalent macro-cellular solution. If the Mobile Operator is also a Fixed Broadband Operator (or have favorable partnership with one) there are in general better cost scaling possible than above would assume (e.g., another AT&T advantage in their DAS / Small Cell strategy).

    In realistic costing scenarios so far, Small Cell economical boundaries are given by the Figure below:

    Let me emphasize that above obviously assumes that an operator have a choice between deploying a Small Cell Network and conventional Cell Split, Nodal Overlay (or co-location on existing cellular site) or Sectorization (if spectral capacity allows). In the Future and in Hot Traffic Zones this might not be the case. Leaving Small Cell Network deployment or letting the customers “suffer” poorer QoS be the only options left to the mobile network operator.

    So how can we (i.e., the Mobile Operator) improve the Economics of Small Cell deployment?

    Having access fixed broadband such as fiber or high-quality cable infrastructure would make the backhaul scaling a lot better. Being a mobile and fixed broadband provider does become very advantageous for Small Cell Network Economics. However, the site lease (and maintenance) scaling remains a problem as lampposts or other interesting Small Cell locations might not scale very aggressively (e.g., there are examples of lamppost leases being as expensive as regular rooftop locations). From a capital investment point of view, I have my doubts whether the price will scale downwards as favorable as they would need to be. Much of the capacity gain comes from very sophisticated antenna configurations that is difficult to see being extremely cheap:

    Small Cell Equipment Suppliers would need to provide a Carrier-grade solution priced at  maximum 1,000EUR all included! to have a fighting chance of making massive small cell network deployment really economical.

    We could assume that most of the “Small Cells” are in fact customers existing private access points (or our customers employers access points) and simply push (almost) all cellular data traffic onto those whenever a customer is in vicinity of such. All those existing and future private access points are (at least in Western Europe) connected to at least fairly good quality fixed backhaul in the form of VDSL, Cable (DOCSIS3), and eventually Fiber. This would obviously improve the TCO of “Small Cells” tremendously … Right?

    Well it would reduce the MNOs TCO (as it shift the cost burden to the operator’s customer or employers of those customers) …Well … This picture also would  not really be Small Cells in the sense of proper designed and integrated cells in the Cellular sense of the word, providing the operator end-2-end control of his customers service experience. In fact taking the above scenario to the extreme we might not need Small Cells at all, in the Cellular sense, or at least dramatically less than using the standard cellular capacity formula above.

    In Qualcomm (as well as many infrastructure suppliers) ultimate vision the 1,000x challenge is solved by moving towards a super-heterogeneous network that consist of everything from Cellular Small Cells, Public & Private WiFi access points as well as Femto cells thrown into the equation as well.

    Such an ultimate picture might indeed make the Small Cell challenge economically feasible. However, it does very fundamentally change the current operational MNO business model and it is not clear that transition comes without cost and only benefits.

    Last but not least it is pretty clear than instead of 3 – 5 MNOs all going out plastering walls and lampposts with Small Cell Nodes & Antennas sharing might be an incredible clever idea. In fact I would not be altogether surprised if we will see new independent business models providing Shared Small Cell solutions for incumbent Mobile Network Operators.

    Before closing the Blog, I do find it instructive to pause and reflect on lessons from Japan’s massive WiFi deployment. It might serves as a lesson to massive Small Cell Network deployment as well and an indication that collaboration might be a lot smarter than competition when it comes to such deployment: