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

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

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

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

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

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

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

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

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

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

### THE MAGIC RECIPE FOR CELLULAR PERFORMANCE.

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

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

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

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

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

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

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

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

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

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

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

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

### WESTERN EUROPE SPECTRUM POSITIONS.

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

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

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

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

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

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

### HOW AND HOW NOT TO WIN BEST NETWORK AWARDS.

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

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

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

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

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

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

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

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

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

### END THOUGHTS.

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

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

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

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

### ACKNOWLEDGEMENT.

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

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

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

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

# Mobile Data-centric Price Plans – An illustration of the De-composed.

How much money would it take for you to give up internet? …for the rest of your life? … and maybe much more important; How much do you want to pay for internet? The following cool video URL “Would you give up the Internet for 1 Million Dollars” hints towards both of those questions and an interesting paradox!

The perception of value is orders of magnitude higher than the willingness to pay, i.e.,

“I would NOT give up Internet for life for a Million+ US Dollars … oh … BUT… I don’t want to pay more than a couple of bucks for it either” (actually for a mature postpaid-rich market the chances are that over your expected life-time you will pay between 30 to 40 thousand US$for mobile internet & voice & some messaging). Price plans are fascinating! … Particular the recent data-centric price plans bundling in legacy services such as voice and SMS. Needles to say that a consumer today often needs an advanced degree in science to really understand the price plans they are being presented. A high degree of trust is involved in choosing a given plan. The consumer usually takes what has been recommended by the shop expert (who most likely doesn’t have an advanced science degree either). This shop expert furthermore might (or might not) get a commission (i.e., a bonus) selling you a particular plan and thus in such a case hardly is the poster child of objectiveness. How does the pricing experts come to the prices that they offer to the consumer? Are those plans internally consistent … or maybe not? It becomes particular interesting to study data-centric price plans that try to re-balance Mobile Voice and SMS. How is 4G (i.e., in Europe also called LTE) being charged versus “normal” data offerings in the market? Do the mobile consumer pay more for Quality? Or maybe less? What is the real price of mobile data? … Clearly, it is not the price we pay for a data-centric price plan. ## A Data-centric Tale of a Country called United & a Telecom Company called Anything Anywhere! As an example of mobile data pricing and in particular of data-centric mobile pricing with Voice and SMS included, I looked at a Western European Market (let’s call it United) and a mobile operator called Anything Anywhere. Anything Anywhere (AA) is known for its comprehensive & leading-edge 4G network as well as several innovative product ideas around mobile broadband data. In my chosen Western European country United, voice revenues have rapidly declined over the last 5 years. Between 2009 to 2014 mobile voice revenues lost more than 36% compared to an overall revenue loss of “only” 14%. This corresponds to a compounded annual growth rate of minus 6.3% over the period. For an in depth analysis of the incredible mobile voice revenue losses the mobile industry have incurred in recent years see my blog “The unbearable lightness of mobile voice”. Did this market experience a massive uptake in prepaid customers? No! Not at all … The prepaid share of the customer base went from ca. 60% in 2009 to ca. 45% in 2014. So in other words the Postpaid base over the period had grown with 15% and in 2014 was around 55%. This should usually have been a cause for great joy and incredible boost in revenues. United is also a market that has largely managed not to capitalize economically on substantial market consolidation. As it is with many other mobile markets, engaging & embracing the mobile broadband data journey has been followed by a sharp decline in the overall share of voice revenue from ca. 70% in 2009 to ca. 50% in 2014. An ugly trend when the total mobile revenue declines as well. The Smartphone penetration in United as of Q1 2014 was ca. 71% with 32% iOS-based devices. Compare this to 2009 where the smartphone penetration was ca. 21% with iOS making out around 75+%. Our Mobile Operator AA has the following price plan structure (note: all information is taken directly from AA’s web site and can be found back if you guess which company it applies to); • Data-centric price plans with unlimited Voice and SMS. • Differentiated speed plans, i.e., 4G (average speed advertised to 12 – 15 Mbps) vs. Double Speed 4G (average speed advertised to 24 – 30 Mbps). • Offer plans that apply Europe Union-wide. • Option to pay less for handsets upfront but more per month (i.e., particular attractive for expensive handsets such as iPhone or Samsung Galaxy top-range models). • Default offering is 24 month although a shorter period is possible as well. • Offer SIM-only data-centric with unlimited voice & SMS. • Offer Data-only SIM-only plans. • Further you will get access to extensive “WiFi Underground”. Are allowed tethering and VoIP including Voice-calling over WiFi. So here is an example of AA’s data-centric pricing for various data allowances. In this illustration I have chosen to add an iPhone 6 Plus (why? well I do love that phone as it largely replaces my iPad outside my home!) with 128GB storage. This choice have no impact on the fixed and variable parts of the respective price plans. For SIM-Only plans in the data below, I have added the (Apple) retail price of the iPhone 6 Plus (light grey bars). This is to make the comparison somewhat more comparable. It should of course be clear that in the SIM-only plans, the consumer is not obliged to buy a new device. • Figure above: illustrates the total consumer cost or total price paid over the period (in local currency) of different data plans for our leading Western European Mobile Operator AA. The first 9 plans shown above includes a iPhone 6 Plus with 128GB memory. The last 5 are SIM only plans with the last 2 being Data-only SIM-only plans. The abbreviations are the following PPM: Pay per Month (but little upfront for terminal), PUF: Pay UpFront (for terminal) and less per month, SIMO: SIM-Only plan, SIMDO: SIM Data-Only plan, xxGB: The xx amount of Giga Bytes offered in Plan, 2x indicates double 4G speed of “normal” and 1x indicates “normal” speed, 1st UL indicates unlimited voice in plan, 2nd UL indicates unlimited SMS in plan, EU indicates that the plan also applies to countries in EU without extra charges. So PPM20GB2xULULEU defines a Pay per Month plan (i.e., the handset is pay over the contract period and thus leads to higher monthly charges) with 20 GB allowance at Double (4G) Speed with Unlimited Voice and Unlimited SMS valid across EU. In this plan you would pay 100 (in local currency) for a iPhone 6 Plus with 128 GB. Note the local Apple Shop retail price of an iPhone 6 Plus with 128 GB is around 789 in local currency (of which ca. 132 is VAT) for this particular country. Note: for the SIM-only plans (i.e., SIMO & SIMDO) I have added the Apple retail price of a iPhone 6 Plus 128GB. It furthermore should be pointed out that the fixed service fee and the data consumption price does not vary with choice of handset. If I decide that I really want that iPhone 6 Plus and I do not want to pay the high price (even with discounts) that some price plans offers. AA offers me a 20GB 4G data-plan, pay 100 upfront for the iPhone 6 Plus (with 128 GB memory) and for the next 24 month 63.99 (i.e., as this feels much cheaper than paying 64) per month. After 24 month my total cost of the 20 GB would be 1,636. I could thus save 230 over the 24 month if I wanted to pay 470 (+370 compared to previous plan & – 319 compared to Apple retail price) for the iPhone. In this lower cost plan my monthly cost of the 20 GB would be 38.99 or 25 (40%!) less on a monthly basis. The Analysis show that a “Pay-less-upfront-and-more-per-month” subscriber would end up after the 24 month having paid at least ca. 761 for the iPhone 6 Plus (with 128GB). We will see later, that the total price paid for the iPhone 6 Plus however is likely to be approximately 792 or slightly above today’s retail price (based on Apple’s pricing). ## The Price of a Byte and all that Jazz So how does the above data-price plans look like in terms of Price-per-Giga-Byte? Although in most cases not be very clear to the consumer, the data-centric price plan is structured around the price of the primary data allowance (i.e., the variable part) and non-data related bundled services included in the plan (i.e., the fixed service part representing non-data items). There will be a variable price reflecting the data-centric price-plans data allowance and a “Fixed” Service Fee that capture the price of bundled services such as voice and SMS. Based on total price of the data-centric price plan, it will often appear that the higher the allowance the cheaper does your unit-data “consumption” (or allowance) become. Indicating that volume discounts have been factored into the price-plan. In other words, the higher the data allowance the lower the price per GB allowance. This is often flawed logic and simply an artefact of the bundled non-data related services being priced into the plan. However, to get to that level of understanding requires a bit of analysis that most of us certainly don’t do before a purchase. • Figure above: Illustrates the unit-price of a Giga Byte (GB) versus AA’s various data-centric price plans. Note the price plans can be decomposed into a variable data-usage attributable price (per GB) and a fixed service fee that accounts for non-data services blended into the price. The Data Consumption per GB is the variable data-usage dependable part of the Price Plan and the Total price per GB is the full price normalized to the plans data consumption allowance. So with the above we have argued that the total data-centric price can be written as a fixed and a variable part; As will be described in more detail below, the data-centric price is structured in what can be characterized as a “Fixed Service Fee” and a variable “Data Consumption Price that depends on a given price-plan’s data allowance (i.e., GB is Giga Byte). The “Data Consumption Price is variable in nature and while it might be a complex (i.e. in terms of complexity) function of data allowance it typically be of the form with the exponent (i.e., Beta) being 1 or close to 1. In other words the Data Consumptive price is a linear (or approximately so) function of the data allowance. In case is larger than 1, data pricing gets progressively more expensive with increasing allowance (i.e., penalizing high consumption or as I believe right-costing high consumption). For lower than 1, data gets progressively cheaper with increasing data allowances corresponding to volume discounts with the danger of mismatching the data pricing with the cost of delivering the data. The “Fixed Service Fee” depends on all the non-data related goodies that are added to the data-centric price plan, such as (a) unlimited voice, (b) unlimited SMS, (c) Price plan applies Europe-wide (i.e., EU-Option), (d) handset subsidy recovery fee, (e) maybe a customer management fee, etc.. For most price data-centric plan, If the data-centric price divided by the allowance would be plotted against the allowance in a Log-Log format would result in a fairly straight-line. Nothing really surprising given the pricing math involved! It is instructive to see what actually happens when we take a data-centric price and divide by the corresponding data allowance; For very large data allowances the price-centric per GB would asymptotically converge to , i.e., the unit cost of a GB. As is usually a lot smaller than , we see that there is another limit, where the allowance is relative low, where we would see the data-centric pricing per GB slope (in a Log-Log plot) become linear in the data allowance. Typically for allowances from 0.1 GB up towards 50 GB, non-linear slope of approximately -0.7±0.1 is observed and thus in between the linear and the constant pricing regime. We can also observe that If the total price, of a data-centric price plan associated with a given data allowance (i.e., GB), is used to derive a price-per-GB, one would conclude that most mobile operators provide the consumer with volume discounts as they adapt higher data allowance plans. The GB gets progressively cheaper for higher usage plans. As most data-centric price plans are in the range where is (a lot) smaller than , it will appear that the unit price of data declines as the data allowance increases. However in most cases it is likely an artefact of the Fixed Service Fee that reflects non-data related services which unless a data-only bundle can be a very substantial part of the data-centric price plan. It is clear that data-allowance normalizing the totality of a data-centric price plan, particular when non-data services have been blended into the plan, will not reveal the real price of data. If used for assessing, for example, data profitability or other mobile data related financial KPIs this approach might be of very little use. • Figure above: illustrates the basic characteristics of a data-centric price plan normalized by the data allowance. The data for this example reflects the AA’s data-centric price plans 2x4G Speed with bundled unlimited Voice & SMS as well as applying EU-wide. We see that the Beta value corresponds to a Volume Discount (at values lower than 1) or a Volume Penalty (at values higher than 1). Oh yeah! … The really “funny” part of most data-price plan analysis (including my own past ones!) are they are more likely to reflect the Fixed Service Part (independent of the Data allowance) of the Data-centric price plan than the actual unit price of mobile data. ## What to expect from AA’s data-centric price plans? so in a rational world of data-centric pricing (assuming such exist) what should we expect of Anything Anywhere’s price plans as advertised online; • The (embedded) price for unlimited voice would be the same irrespective of the data plan’s allowed data usage (i.e., unlimited Voice does not depend on data plan). • The (embedded) price for unlimited SMS would be the same irrespective of the data plan’s allowed data usage (i.e., unlimited SMS does not depend on data plan). • You would pay more for having your plan extended to apply across Europe Union compared to not having this option. • You would (actually you should) expect to pay more per Mega Byte for the Double Speed option as compared to the Single Speed Option. • If you decide to “finance” your handset purchase (i.e., pay less upfront option) within a data plan you should expect to pay more on a monthly basis. • Given a data plan has a whole range of associated handsets priced From Free (i.e., included in plan without extra upfront charge) to high-end high-priced Smartphones, such as iPhone 6 Plus 128 GB, you would not expect that handset related cost would have been priced into the data plan. Or if it is, it must be the lowest common denominator for the whole range of offered handsets at a given price plan. • Where the discussion becomes really interesting is how your data consumption should be priced; (1) You pay more per unit of data consumption as you consume more data on a monthly basis, (2) You pay the same per unit irrespective of your consumption or (3) You should have a volume discount making your units cheaper the more you consume. of course the above is if and only if the price plans have been developed in reasonable self-consistent manner. • Figure above: Illustrates AA’s various data-centric price plans (taken from their web site). Note that PPM represents low upfront (terminal) cost for the consumer and higher monthly cost and PUF represent paying upfront for the handset and thus having lower monthly costs as a consequence. The Operator AA allows the consumer in the PPM Plan to choose for an iPhone 6 Plus 128GB (priced at 100 to 160) or an IPhone 6 Plus 64GB option (at a lower price of course). First note that Price Plans (with more than 2 data points) tend to be linear with the Data Usage allowance. ## The Fixed Service Fee – The Art of Re-Capture Lost legacy Value? In the following I define the Fixed Service Fee as the part of the total data-centric price plan that is independent of a given plan’s data allowance. The logic is that this part would contain all non-data related cost such as Unlimited Voice, Unlimited SMS, EU-Option, etc.. From AA’s voice plan (for 250 Minutes @ 10 per Month & 750 Minutes @ 15 per Month) with unlimited SMS (& no data) it can be inferred that • Price of Unlimited SMS can be no higher than 7.5. This however is likely also include general customer maintenance cost. Monthly customer maintenance cost (cost of billing, storage, customer care & systems support, etc.) might be deduced from the SIM-Only Data-Only package and would be • Price of Monthly Customer Maintenance could be in the order of 5, which would imply that the Unlimited SMS price would be 2.5. Note the market average Postpaid SMS ARPU in 2014 was ca., 8.40 (based on Pyramid Research data). The market average number of postpaid SMS per month was ca. 273 SMS. From AA’s SIM-only plan we get that the fixed portion of providing service (i.e., customer maintenance, unlimited Voice & SMS usage) is 14 and thus • Price of Unlimited Voice should be approximately 6.5. Note the market average Postpaid Voice ARPU was ca. 12 (based on Pyramid Research data). The market average voice usage per month was ca. 337 minutes. Further from the available limited voice price plans it can be deduced that unlimited voice must be higher than 1,000 Minutes or more than 3 times the national postpaid average. The fixed part of the data-centric pricing difference between the data-centric SIM-only plan and similar data-centric plan including a handset (i.e., all services are the same except for the addition of the handset) could be regarded as a minimum handset financing cost allowing the operator to recover some of the handset subsidy • Equipment subsidy recovery cost of 7 (i.e., over a 24 month period this amounts to 168 which is likely to recover the average handset subsidy). Note is the customer chooses to pay little upfront for the handset, the customer would have to pay 26 extra per month in he fixed service fee. Thus low upfront cost result in another 624 over the 24 month contract period. Interestingly is that with the initial 7 for handset subsidy recovery in the basic fixed service fee a customer would have paid 792 in handset recovery over 24 month period the contract applies to (a bit more than the iPhone 6 Plus 128GB retail price). The price for allowing the data-centric price-plan to apply Europe Union Wide is • The EU-Option (i.e., plan applicable within EU) appears to be priced at ca. 5 (caution: 2x4G vis-a-vis 1x4G could have been priced into this delta as well). For EU-option price it should be noted here that the two plans that are being compared differs not only in the EU-option. The plan without the EU option is a data plan with “normal” 4G speed, while the EU-option plan supports double 4G speeds. So in theory the additional EU-option charge of 5 could also include a surcharge for the additional speed. Why an operator would add the double speed to the fixed Service Fee price part is “bit” strange. The 2x4G speed price-plan option clearly is a variable trigger for cost (and value to the customer’s data usage). Thus should be introduced in the the variable part (i.e., the Giga-Byte dependent part) of the data-centric price plan. It is assumed that indeed the derived difference can be attributed to the EU-option, i.e., the double speed has not been include in the monthly Fixed Service Fee. In summary we get AA’s data-centric price plan’s monthly Fixed Service Fee de-composition as follows; • Figure above: shows the composition of the monthly fixed service fee as part of AA’s data-centric plans. Of course in a SIM-only scenario the consumer would not have the Handset Recovery Fee inserted in the price plan. So irrespective of the data allowance a (postpaid) customer would pay between 26 to 52 per month depending on whether handset financing is chosen (i.e., Low upfront payment on the expense of higher monthly cost). Mobile data usage still has to happen! ## The price of Mobile Data Allowance. The variable data-price in the studied date-centric price plans are summarized in the table below as well as the figure; #### Price-plan #### 4G Speed #### Price per GB Pay Less Upfront More per Month Double 0.61±0.03 Pay Upfront & Less per Month Double 0.67±0.05 SIM-Only Single 1.47±0.08 SIM-Only Data Only Single 2 (only 2 data points) The first thing that obviously should make you Stop in Wonder is that Single 4G Speed Giga Byte is more than Twice the price of a Double 4G Speed Giga Byte In need for speed … well that will give you a pretty good deal with AA’s price 2x4G plans. Second thing to notice is that it would appear to be a really bad deal (with respect to the price-per-byte) to be a SIM-Only Data-Only customer. The Data-Only pays 2 per GB. Almost 3 times more than if you would choose a subscription with a device, double speed, double unlimited and EU-wide applicable price plan. Agreed! In absolute terms the SIM-only Data-only cost a lot less per month (9 less than the 20GB pay device upfront) and it is possible to run away after 12 months (versus the 24 month plans). One rationale for charging extra per Byte for a SIM-only Data-only plan could be that the SIM card might be used in Tablet or Data-card/Dongle products that typically does consume most if not all of a given plans allowance. For normal devices and high allowance plans on average the consumption can be quiet a lot lower than the actual allowance. Particular over a 24 month period. You might argue that this is all about how the data-centric price plans have been de-composed in a fixed service fee (supposedly the non-data dependent component) and a data consumptive price. However, even when considering the full price of a given price plan is the Single-4G-Speed more expensive per Byte than Double-4G-Speed. You may also argue that I am comparing apples and oranges (or even bananas pending taste) as the Double-4G-Speed plans include a devices and a price-plan that applies EU-wide versus the SIM-only plan that includes the customers own device and a price-plan that only works in United. All true of course … Why that should be more expensive to opt out of is a bit beyond me and why this should have an inflationary impact on the price-per-Byte … well a bit of a mystery as well. At least there is no (statistical) difference in the variable price of a Giga Byte whether the customer chooses to pay of her device over the 24 month contract period or pay (most of) it upfront. For AA it doesn’t seem to be of concern! …. As 88% would come back for more (according with their web site). Obviously this whole analysis above make the big assumption that the data-centric price plans are somewhat rationally derived … this might not be the case! and it assumes that rationally & transparently derived price plans are the best for the consumer … and it assumes what is good for the consumer is also good for the company … Is AA different in this respect to that of other Operators around the world … No! AA is not different from any other incumbent operator coming from a mobile voice centric domain! ### Acknowledgement I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. ## Postscript – The way I like to look at (rational … what ever that means) data-centric pricing. Firstly, it would appear that AA’s pricing philosophy follows the industry standard of pricing mobile services and in particular mobile data-centric services by the data volume allowance. Non-data services are added to the data-centric price plan and in all effect make up for the most part of the price-plan even at relative higher data allowances; • Figure above: illustrates the typical approach to price plan design in the Telecom’s industry. Note while not per se wrong it often overweight’s the volume element of pricing and often results in sub-optimizing the Quality and Product aspects . Source: Dr. Kim K Larsen’s Mind Share contribution at Informa’s LTE World Summit May 2012; “Right pricing LTE and mobile broadband in general (a Technologist’ Observations)”. Unlimited Voice and SMS in AA’s standard data-centric plans clearly should mitigate possible loss or migration away from old fashion voice (i.e., circuit switched) and SMS. However both the estimated allowances for unlimited voice (6.5) and SMS (2.5) appear to be a lot lower than their classical standalone ARPUs for the postpaid category. This certainly could explain that this market (as many others in Western Europe) have lost massive amount of voice revenues over the last 5 years. In other words re-capturing or re-balancing legacy service revenues into data-centric plans still have some way to go in order to be truly effective (if at all possible which is highly questionable at this time and age). As a Technologist, I am particular interested in how the technology cost and benefits are being considered in data-centric price plans. The big challenge for the pricing expert who focus too much on volume is that the same volume can result from vastly different network qualities and speed. The customers handset will drive the experience of quality and certainly consumption. By that differences in network load and thus technology cost. A customer with a iPhone 6 Plus is likely to load the mobile data network more (and thus incur higher cost) than a customer with a normal screen smartphone of 1 or 2 generations removed from iPhone 6 Plus. It is even conceivable that a user with iPhone 6 Plus will load the network more than a customer with a normal iPhone 6 (independent of the iOS). This is very very different for Voice and SMS volumetric considerations in legacy price plans, where handset had little (or no) impact on network load relative to the usage. ### For data-centric price plans to be consistent with the technology cost incurred one should consider; • Higher “guarantied” Quality, typically speed or latency, should be priced higher per Byte than lower quality plans (or at the very least not lower). • Higher Volumetric Allowances should be priced per Byte higher than Lower Volumetric Allowance (or at the very least not lower). • Offering unlimited Voice & SMS in data-centric plans (as well as other bundled goodies) should be carefully re-balanced to re-capture some of lost legacy revenues. That AA’s data-centric plans for double speed appears to be cheaper than their plans at a lower data delivery quality level is not consistent with costing. Of course, AA cannot really guaranty that the customer will get double 4G speed everywhere and as such it may not be fair to charge substantially more than for single speed. However, this is of course not what appear to happen here. AA’s lowest data unit price (in per Giga Byte) is around 0.6 – 0.7 (or 0.06 – 0.07 Cent per Mega Byte). That price is very low and in all likelihood lower than their actual production cost of a GB or MB. However, one may argue that as long as the Total Service Revenue gained by a data-centric price plan recover the production cost, as well as providing a healthy margin then whether the applied data unit-price is designed to recover the data production cost is maybe less of an issue. In other words, data profitability may not matter as much as overall profitability. This said it remains in my opinion in-excusable for a mobile operator not to understand its main (data) cost drivers and ensure it is recovered in their overall pricing strategies. Surely! You may say? … “Surely Mobile Operators know their cost structure and respective cost drivers and their price plans reflects this knowledge?” It is my observation that most price plans (data-centric or not) are developed primarily in response to competition (which of course is an important pricing element as well) rather than firmly anchored in Cost, Value & Profit considerations. Do Operators really & deeply know their own cost structure and cost drivers? … Ahhh … In my opinion few really appear to do! # 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. THE MISSING LINK 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. PRE-AMP 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. BUT how economical will this journey be for the Mobile Network Operator? THE ECONOMICS OF THE THOUSAND TIMES CHALLENGE 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: 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! THE ECONOMICS OF SPECTRUM. 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! Spectrum: 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 ECONOMICS OF SPECTRAL EFFICIENCY

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.

THE ECONOMICS OF INCREASED CELL SITE DENSITY

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.

However,

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.

EXAMPLE:

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: