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

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

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

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

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

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

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

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

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

THE MAGIC RECIPE FOR CELLULAR PERFORMANCE.

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

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

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

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

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

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

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

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

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

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

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

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

WESTERN EUROPE SPECTRUM POSITIONS.

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

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

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

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

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

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

HOW AND HOW NOT TO WIN BEST NETWORK AWARDS.

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

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

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

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

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

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

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

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

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

END THOUGHTS.

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

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

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

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

ACKNOWLEDGEMENT.

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

FURTHER READINGS.

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

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

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