- Up-to 50% of Sites in Mobile Networks captures no more than 10% of Mobile Service Revenues.
- The “Ugly” (cost) Tail of Cellular Networks can only be remedied by either removing sites (and thus low- or –no-profitable service) or by aggressive site sharing.
- With Network Sharing expect up-to 35% saving on Technology Opex as well as future Opex avoidance.
- The resulting Technology Opex savings easily translates into a Corporate Opex saving of up-to 5% as well as future Opex avoidance.
- Active as well as Passive Network Sharing brings substantial Capex avoidance and improved sourcing economics by improved scale.
- National Roaming can be an alternative to Network Sharing in low traffic and less attractive areas. Capex attractive but a likely Ebitda-pressure point over time.
- “Sharing by Towerco” can be an alternative to Real Network Sharing. It is an attractive mean to Capex avoidance but is not Ebitda-friendly. Long-term commitments combined with Ebitda-risks makes it a strategy that should to be considered very carefully.
- Network Sharing frees up cash to be spend in other areas (e.g., customer acquisition).
- Network Sharing structured correctly can result in faster network deployment –> substantial time to market gains.
- Network Sharing provides substantially better network quality and capacity for a lot less cash (compared to standalone).
- Instant cell split option easy to realize by Network Sharing –> cost-efficient provision of network capacity.
- Network Sharing offers enhanced customer experience by improved coverage at less economics.
- Network Sharing can bring spectral efficiency gains of 10% or higher.
The purpose of this story is to provide decision makers, analysts and general public with some simple rules that will allow them to understand Network Sharing and assess whether it is likely to be worthwhile to implement and of course successful in delivering the promise of higher financial and operational efficiency.
Today’s Technology supports almost any network sharing scenario that can be thought of (or not). Financially & not to forget Strategically this is far from so obvious.
Network Sharing is not only about Gains, its evil twin Loss is always present.
Network Sharing is a great pre-cursor to consolidation.
Network sharing has been the new and old black for many years. It is a fashion that that seems to stay and grow with and within the telecommunications industry. Not surprising as we shall see that one of the biggest financial efficiency levers are in the Technology Cost Structure. Technology wise there is no real stumbling blocks for even very aggressive network sharing maximizing the amount of system resources being shared, passive as well as active. The huge quantum-leap in availability of very high quality and affordable fiber optic connectivity in most mature markets, as well between many countries, have pushed the sharing boundaries into Core Network, Service Platforms and easily reaching into Billing & Policy Platforms with regulatory and law being the biggest blocking factor of Network-as-a-Service offerings. Below figure provides the anatomy of network sharing. It should of course be noted that also within each category several flavors of sharing is possible pending operator taste and regulatory possibilities.
Network Sharing comes in many different flavors. To only consider one sharing model is foolish and likely will result in wrong benefit assessment. Setting a sharing deal up for failure down the road (if it ever gets started). It is particular important to understand that while active sharing provides the most comprehensive synergy potential, it tends to be a poor strategy in areas of high traffic potential. Passive sharing is a much more straightforward strategy in such areas. In rural areas, where traffic is less of an issue and profitability is a huge challenge, aggressive active sharing is much more interesting. One should even consider frequency sharing if permitted by regulatory authority. The way I tend to look at the Network Sharing Flavors are (as also depicted in the Figure below);
- Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
- Coverage Limited Areas (i.e., some urban environments, mainly sub-urban and rural) – Minimum Passive Sharing should be pursued with RAN (Active) Sharing providing an additional economical advantage.
- Rural Areas – National Roaming or Full RAN sharing including frequency sharing (if regulatory permissible).
One of the first network sharing deals I got involved in was back in mid-2001 in The Netherlands. This was at the time of the Mobile Industry’s first real cash crises. Just as we were about to launch this new exiting mobile standard (i.e., UMTS) that would bring Internet to the pockets of the masses. After having spend billions & billions of dollars (i.e., way too much of course) on high-frequency 2100MHz UMTS spectrum, all justified by an incredible optimistic (i.e., said in hindsight!) belief in the mobile internet business case, the industry could not afford to deploy the networks required to make our wishful thinking come true.
T-Mobile (i.e., aka Ben BV) engaged with Orange (i.e., aka Dutchtone) in The Netherlands on what should have been a textbook example of the perfect network sharing arrangement. We made a great business case for a comprehensive network sharing. It made good financial and operational sense at the setup. At the time the sharing game was about Capex avoidance and trying to get the UMTS network rolled out as quickly as possible within very tight budgets imposed by our mother companies (i.e., Deutsche Telekom and France Telecom respectively). Two years down the road we revised our strategic thoughts on network sharing. We made another business case for why deploying on standalone made more sense than sharing. At that time the only thing T-we (Mobile NL) really could agree with Orange NL about was ancillary cabinet sharing and of course the underlying site sharing. Except for agreeing not to like the Joint Venture we created (i.e., RANN BV), all else were at odds, e.g., supplier strategy, degree of sharing, network vision, deployment pace, etc… Our respective deployment strategies had diverged so substantially from each other that sharing no longer was an option. Further, T-Mobile decided to rely on the ancillary cabinet we had in place for GSM –> so also no ancillary sharing. This was also at a time where cabinets and equipment took up a lot of space (i.e., do you still remember the first & 2nd generation 3G cabinets?). Many site locations simply could not sustain 2 GSM and 2 UMTS solutions. Our site demand went through the roof and pretty much killed the sharing case.
- Starting point: Site Sharing, Shared Built, Active RAN and transport sharing.
- Just before breakup I: Site Sharing, cabinet sharing if required, shared built where deployment plans overlapped.
- Just before breakup II:Crisis over and almost out. Cash and Capex was no longer as critical as it was at startup.
It did not help that the Joint Venture RANN BV created to realize T-Mobile & Orange NL shared UMTS network plans frequently were at odds with both founding companies. Both entities still had their full engineering & planning departments including rollout departments (i.e., in effect we tried to coordinate across 3 rollout departments & 3 planning departments, 1 from T-Mobile, 1 from Orange and 1 from RANN BV … pretty silly! Right!). Eventually RANN BV was dissolved. The rest is history. Later T-Mobile NL acquired Orange NL and engaged in a very successful network consolidation (within time and money).
The economical benefits of Sharing and Network Consolidation are pretty similar and follows pretty much the same recipe.
Luckily (if Luck has anything to do with it?) since then there have been more successful sharing projects although the verdict is still out whether these constructs are long-lived or not and maybe also by what definition success is measured.
Judging from the more than 34 Thousand views on my various public network sharing presentations, I have delivered around the world since 2008, there certainly seem to be a strong and persistent interest in the topic.
- Fundamentals of Mobile Network Sharing.(2012).
- Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. (2012).
- Economics of Network Sharing. (2008).
- Technology Cost Optimization Strategies. (2009).
- Analyzing Business Models for Network Sharing Success. (2009).
I have worked on Network Sharing and Cost Structure Engineering since the early days of 2001. Very initially focus was on UMTS deployments, the need and requirements to deploy much more cash efficient. Cash was a very scarce resource after the dot-com crash between 2000 & 2003. After 2004 the game changed to be an Opex Saving & Avoidance game to mitigate stagnating customer growth and revenue growth slow down.
I have in detail studied many Network Sharing strategies, concepts and deals. A few have turned out successful (at least still alive & kicking) and many more un-successful (never made it beyond talk and analysis). One of the most substantial Network Sharing deals (arguable closer to network consolidation), I work on several years ago is still very much alive and kicking. That particular setup has been heralded as successful and a poster-boy example of the best of Network Sharing (or consolidation). However, by 2014 there has hardly been any sites taken out of operation (certainly no where close to the numbers we assumed and based our synergy savings on).
More than 50% of all network related TCO comes from site-related operational and capital expenses.
Despite the great economical promises and operational efficiencies that can be gained by two mobile operations (fixed for that matter as well) agreeing to share their networks, it is important to note that
It is NOT enough to have a great network sharing plan. A very high degree of discipline and razor-sharp focus in project execution is crucial for delivering network sharing within money and time.
With introduction of UMTS & Mobile Broadband the mobile operator’s margin & cash have come under increasing pressure (not helped by voice revenue decline & saturated markets).
Technology addresses up-to 25% of a Mobile Operators Total Opex & more than 90% of the Capital Expenses.
Radio Access Networks accounts easily for more than 50% of all Network Opex and Capex.
For a reasonable efficient Telco Operation, Technology Cost is the most important lever to slow the business decline, improve financial results and return on investments.
Above Profit & Loss Figure serves as an illustration that Technology Cost (Opex & Capex) optimization and is pivotal to achieve a more efficient operation and a lot more certain that relying on new business (and revenue) additions
It is not by chance that RAN Sharing is such a hot topic. The Radio Access Network takes up more than half of Network Cost including Capex.
Of course there are many other general cost levers to consider that might be less complex than Network Sharing to implement. Another Black (or Dark Grey) is outsourcing of (key) operational functions to a 3rd party. Think here about some of the main ticks
- Site acquisition (SA) & landlord relations (LR) – Standard practice for SA, not recommended for landlord relations. Usually better done by operator self (at least while important during deployment)..
- Site Build – Standard practice with sub-contractors..
- Network operations & Maintenance – Cyclic between in-source and outsource pending business cycle.
- Field services – standard practice particular in network sharing scenarios.
- Power management – particular interesting for network sharing scenarios with heavy reliance of diesel generators and fuel logistics (also synergetic with field services).
- Operational Planning – particular for comprehensive managed network services. Network Sharing could outsource RAN & TX Planning.
- Site leases – Have a site management company deal with site leases with a target to get them down with x% (they usually take a share of the reduced amount). Care should be taken not to jeopardize network sharing possibilities. Will impact landlord relations.
- IT operations – Cyclic between in-source and outsource pending business cycle.
- IT Development – Cyclic between in-source and outsource pending business cycle.
- Tower Infrastructure – Typical Cash for infrastructure swap with log-term Opex commitments. Care must be taken to allow for Network Sharing and infrastructure termination.
In general many of the above (with exception of IT or at least in a different context than RAN Sharing) potential outsourcing options can be highly synergetic with Network Sharing and should always be considered when negotiating a deal.
Looking at the economics of managed services versus network sharing we find in general the following picture;
and remember that any managed services that is assumed to be applicable in the Network Sharing strategy column will enable the upper end of the possible synergy potential estimated. Having a deeper look at the original T-Mobile UK and Hutchinson UK 3G RAN Sharing deal is very instructive as it provides a view on what can be achieved when combining both best practices of network sharing and shared managed services (i.e., this is the story for The ABC of Network Sharing – Part II).
Seriously consider Managed Services when it can be proven to provide at least 20% Opex synergies will be gained for apples to apples SLAs and KPIs (as compared to your insourced model).
Do your Homework! It is bad Karma to implement Managed Services on an in-efficient organizational function or area that has not been optimized prior to outsourcing.
Do your Homework (Part II)! Measure, Analyze and Understand your own relevant cost structure 100% before outsourcing!
It is not by chance that Deutsche Telekom AG (DTAG) has been leading the Telco Operational Efficiency movement and have some of the most successful network sharing operations around. Since 2004 DTAG have had several (very) deep dives programs into their cost structure and defining detailed initiatives across every single operation as well as on its Group level. This has led to one of the most efficient Telco operations around in Western Europe & the US and with lots to learn from when it comes to managing your cost structure when faced with stagnating revenue growth and increasing cost pressure.
In 2006, prior to another very big efficiency program was kicked off within DTAG, I was asked to take a very fundamental and extreme (but nevertheless realistic) look at all the European mobile operations technology cost structures and come back with how much Technology Opex could be pulled out of them (without hurting the business) within 3-4 years (or 2010).
Below (historical) Figure illustrates my findings from 2006 (disguised but nevertheless the real deal);
This analysis (7-8 years old by now) directly resulted in a lot of Network Sharing discussions across DTAGs operations in Europe. Ultimately this work led to a couple of successful Network Sharing engagements within the DTAG (i.e., T-Mobile) Western European footprint. It enabled some of the more in-efficient mobile operations to do a lot more than they could have done standalone and at least one today went from a number last to number 1. So YES … Network Sharing & Cost Structure Engineering can be used to leapfrog an in-efficient business and by that transforming an ugly duckling into what might be regarded as an approximation of a swan. (in this particular example I have in mind, I will refrain from calling it a beautiful swan … because it really isn’t … although the potential is certainly remain even more today).
The observant reader till see that the order of things (or cost structure engineering) matters. As already said above, the golden rule of outsourcing and managed services is to first ensure you have optimized what can be done internally and then consider outsourcing. We found that first outsourcing network operations or establish a managed service relationship prior to a network sharing relationship was sub-optimal and actually might be hindering reaching the most optimal network sharing outcome (i.e., full RAN sharing or active sharing with joint planning & operations).
Revenue Growth will eventually slow down and might even decline due to competitive climate, poor pricing management and regulatory pressures, A Truism for all markets … its just a matter of time. The Opex Growth is rarely in synch with the revenue slow down. This will result in margin or Ebitda pressure and eventually profitability decline.
Revenue will eventually stagnate and likely even enter decline. Cost is entropy-like and will keep increasing.
The technology refreshment cycles are not only getting shorter. These cycles imposes additional pressure on cash. Longer return on investment cycles results compared to the past. Paradoxical as the life-time of the Mobile Telecom Infrastructure is shorter than in the past. This vicious cycle requires the industry to leapfrog technology efficiency, driving demand for infrastructure sharing and business consolidation as well as new innovative business models (i.e., a topic for another Blog).
The time Telco’s have to return on new technology investments is getting increasingly shorter.
Cost saving measures are certain by nature. New Business & New (even Old) Revenue is by nature uncertain.
Back to NETWORK SHARING WITH A VENGENCE!
I have probably learned more from the network sharing deals that failed than the few ones that succeeded (in the sense of actually sharing something). I have work on sharing deals & concepts across across the world; in Western Europe, Central Eastern Europe, Asia and The USA under very different socio-economical conditions, financial expectations, strategic incentives, and very diverse business cycles.
It is fair to say that over the time I have been engaged in Network Sharing Strategies and Operational Realities, I have come to the conclusion that the best or most efficient sharing strategy depends very much on where an operator’s business cycle is and the network’s infrastructure age.
The benefits that potentially can be gained from sharing will depend very much on whether you
- Greenfield: Initial phase of deployment with more than 80% of sites to be deployed.
- Young: Steady state with more than 80% of your sites already deployed.
- Mature: Just in front of major modernization of your infrastructure.
The below Figure describes the three main cycles of network sharing.
It should be noted that I have omitted the timing benefit aspects from the Rollout Phase (i.e., Greenfield) in the Figure above. The omission is on purpose. I believe (based on experience) that there are more likelihood of delay in deployment than obvious faster time-to-market. This is inherent in getting everything agreed as need to be agreed in a Greenfield Network Sharing Scenario. If time-to-market matters more than initial cost efficiency, then network sharing might not a very effective remedy. Once launch have been achieved and market entry secured, network sharing is an extremely good remedy in securing better economics in less attractive areas (i.e., typical rural and outer sub-urban areas). There are some obvious and very interesting games that can be played out with your competitor particular in the Rollout Phase … not all of them of the Altruistic Nature (to be kind).
There can be a very good strategic arguments of not sharing economical attractive site locations depending on the particular business cycle and competitive climate of a given market. The value certain sites market potential could justify to not give them up for sharing. Particular if competitor time-to-market in those highly attractive areas gets delayed. This said there is hardly any reason for not sharing rural sites where the Ugly (Cost) Tail of low or no profitable sites are situated. Being able to share such low-no-profitability sites simply allow operators to re-focus cash on areas where it really matters. Sharing allows services can be offered in rural and under-develop areas at the lowest cost possible. Particular in emerging markets rural areas, where a fairly large part of the population will be living, the cost of deploying and operating sites will be a lot more expensive than in urban areas. Combined with rural areas substantially lower population density it follows that sites will be a lot harder to make positively return on investment within their useful lifetime.
Total Cost of Ownership of rural sites are in many countries substantially higher than their urban equivalents. Low or No site profitability follows.
In general it can be shown that between 40% to 50% of mature operators sites generates less than 10% of the revenue and are substantially more expensive to deploy and operate than urban sites.
The ugly (cost) tail is a bit more “ugly” in mature western markets (i.e., 50+% of sites) than in emerging markets, as the customers in mature markets have higher coverage expectations in general.
(Source: Western European market. Similar Ugly-tail curves observed in many emerging markets as well although the 10% breakpoint tend to be close to 40%).
It is always recommend to analyze the most obvious strategic games that can be played out. Not only from your own perspective. More importantly, you need to have a comprehensive understanding of your competitors (and sharing partners) games and their most efficient path (which is not always synergetic or matching your own). Cost Structure Engineering should not only consider our own cost structure but also those of your competitors and partners.
Sharing is something that is very fundamental to the human nature. Sharing is on the fundamental level the common use of a given resource, tangible as well as intangible.
Sounds pretty nice! However, Sharing is rarely altruistic in nature i.e., lets be honest … why would you help a competitor to get stronger financially and have him spend his savings for customer acquisition … unless of course you achieve similar or preferably better benefits. It is a given that all sharing stakeholders should stand to benefit from the act of sharing. The more asymmetric perceived or tangible sharing benefits are the less stable will a sharing relationship be (or become over time if the benefit distribution should change significantly).
Recipe for a successful sharing partnership is that the sharing partners both have a perception of a deal that offers reasonable symmetric benefits.
It should be noted that perception of symmetric benefits does not mean per see that every saving or avoidance dollar of benefit is exactly the same for both partners. One stakeholder might get access to more coverage or capacity faster than in standalone. The other stakeholder might be able to more driven by budgetary concerns and sharing allows more extensive deployment than otherwise would have been possible within allocated budgets.
Historical most network sharing deals have focused on RAN Sharing, comprising radio access network (RAN) site locations, related passive infrastructure (e.g., such as tower, cabinets, etc..) and various degrees of active sharing. Recent technology development such as software definable network (SDN), virtualization concepts (e.g., Network Function Virtualization, NFV) have made sharing of core network and value-add service platforms interesting as well (or at least more feasible). Another financially interesting industry trend is to spin-off an operators tower assets to 3rd party Tower Management Companies (TMC). The TMC pays upfront a cash equivalent of the value of the passive tower infrastructure to the Mobile Network Operator (MNO). The MNO then lease (i.e., Opex) back the tower assets from the TMC. Such tower asset deals provide the MNO with upfront cash and the TMC a long-term lease income from the MNO. In my opinion such Tower deals tend to be driven by MNOs short-term cash needs without much regard for longer term profitability and Ebitda (i.e., Revenue minus Opex) developments.
With ever increasing demand for more and more bandwidth feeding our customers mobile internet consumption, fiber optical infrastructures have become a must have. Legacy copper-based fixed transport networks can no longer support such bandwidth demands. Over the next 10 years all Telco’s will face massive investments into fiber-optic networks to sustain the ever growing demand for bandwidth. Sharing such investments should be obvious and straightforward. In this area we also are faced with the choice of passive (Dark Fiber itself) as well as active (i.e., DWDM) infrastructure sharing.
NETWORK SHARING SUCCESS FACTORS
There are many consultants out there who evangelize network sharing as the only real cost reduction / saving measure left to the telecom industry. In Theory they are not wrong. The stories that will be told are almost too good to be true. Are you “desperate” for economical efficiency? You might then get very exited by the network sharing promise and forget that network sharing also has a cost side to it (i.e., usually forget and denial are fairly interchangeable here).
In my experience Network Sharing boils down to the following 4 points:
- Who to share with? (your equal, your better or your worse).
- What to share? (sites, passives, active, frequencies, new sites, old sites, towers, rooftops, organization, ,…).
- Where to share? (rural, sub-urban, urban, regional, all, etc..).
- How to share? (“the legal stuff”).
In my more than 14 years of thinking about and working on Network Sharing I have come to the following heuristics of the pre-requisites a successful network sharing:
- CEOs agree with & endorse Network Sharing.
- Sharing Partners have similar perceived benefits (win-win feel).
- Focus on creating a better network for less and with better time-to-market..
- Both parties share a similar end-goal and have a similar strategic outlook.
While it seems obvious it is often forgotten that Network Sharing is a very-long term engagement (“for Life!”) and like in any other relationship (particular the JV kind) Do consider that a break-up can happen … so be prepared (i.e., “legal stuff”).
Compared to 14 – 15 years ago, Technology pretty much support Network Sharing in all its flavors and is no longer a real show-stopper for engaging with another operator to share network and ripe of (eventually) the financial benefits of such a relationship. References on the technical options for network sharing can be found in the 3GPP TR 3GPP TS 22.951 (“Service Aspects and Requirements for network sharing”) and 123.251 (“Network Sharing; Architecture and Functional Description”). Obviously, today 3GPP support for network sharing runs through most of the 3GPP technical requirements and specification documents.
Technology is not a show-stopper for Network Sharing. The Economics might be!
COST STRUCTURE CONSIDERATIONS.
Before committing man power to a network sharing deal, there are a couple of pretty basic “litmus tests” to be done to see whether the economic savings being promised make sense.
First understand your own cost structure (i.e., Capex, Opex, Cash and Revenues) and in particular where Network Sharing will make an impact – positive as well as negative. I am more often that not, surprised how few Executives and Senior Managers really understand their own company’s cost structure. Thus they are not able to quickly spot un-realistic financial & operational promises made.
Seek answers to the following questions:
- What is the Total Technology Opex (Network & IT) share out of the Total Corporate Opex?
- What is the Total Network Opex out of Total Technology Opex?
- What is the Total Radio Access Network (RAN) Opex out of the Total Network Opex?
- Out of the Total RAN Opex how much relates to sites including Operations & Maintenance?
In general, I would expect the following answers to the above questions based on many of mobile operator cost structure analysis across many different markets (from mature to very emerging, from Western Europe, Central Eastern & Southern Europe, to US and Asia-Pacific).
- Technology Opex is 20% to 25% of Total Corporate Opex defined as “Revenue-minus-Ebitda”(depends a little on degree of leased lines & diesel generator dependence).
- Network Opex should be between 70% to 80% of the Technology Opex.,
- RAN related Opex should be between 50% to 80% of the Network Opex. Of course here it is important to understand that not all of this Opex might be impacted by Network Sharing or at least the impact would depend on the Network Sharing model chosen (e.g., active versus passive).
Lets assume that a given RAN network sharing scenario provides a 35% saving on Total RAN Opex, that would be 35% (RAN Saving) x 60% (RAN Opex) x 75% (Network Opex) x 25% (Technology Opex) which yields a total network sharing saving of 4% on the Corporate Opex.
A saving on Opex obviously should translate into a proportional saving on Ebitda (i.e., Earnings before interest tax depreciation & amortization). The margin saving is given as follows
(with E1 and E2 represents Ebitda before and after the relative Opex saving x, m1 is the margin before the Opex saving, assuming that Revenue remains unchanged after Opex saving has been realized).
From the above we see that when the margin is exactly 50% (i.e., fairly un-usual phenomenon for most mature markets), a saving in Opex corresponds directly to an identical relative saving in Ebitda. When the margin is below 50% the relative impact on Ebitda is higher than the relative saving on Opex. If your margin was 40% prior to a realized Opex saving of 5%, one would expect the margin (or Ebitda) saving to be 1.5x that saving or 7.5%.
In general I would expect up-to 35% Opex saving on relevant technology cost structure from network sharing on established networks. If much more saving is claimed, we should get skeptical of the analysis and certainly not take it on face value. It is not un-usual to see Network Sharing contributing as much as 20% saving (and avoidance on run-rate) on the overall Network Opex (ignoring IT Opex here!).
Why not 50% saving (or avoidance)? You may ask! But only once please!
After all we are taking 2 RAN networks and migrating them into 1 network … surely that should result in at 50% saving (i.e., always on relevant cost structure).
First of all, not all relevant (to cellular sites) cost structure is in general relevant to network sharing. Think here about energy consumption and transport solutions as the most obvious examples. Further, landlords are not likely to allow you to directly share existing site locations, and thus site lease cost with another operator without asking for an increased lease (i.e., 20% to 40% is not un-heard of). Existing lease contracts might need to be opened up to allow sharing, terms & conditions will likely need to be re-negotiated, etc.. in the end site lease savings are achievable but these will not translate into a 50% saving.
WARNING! 50% saving claims as a result of Network Sharing are not to be taken at face value!
Another interesting effect is that more shared sites will eventually result compared to the standalone number of sites. In other words, the shared network will have sites than either of the two networks standalone (and hopefully less than the combined amount of sites prior to sharing & consolidation). The reason for this is that the two sharing parties networks rarely are completely symmetric when it comes to coverage. Thus the shared network that will be somewhat bigger than compared to the standalone networks and thus safeguard the customer experience and hopefully the revenue in a post-merged network scenario. If the ultimate shared network has been planned & optimized properly, both parties customers will experience an increased network quality in terms of coverage and capacity (i.e., speed).
#SitesA , #SitesB < #SitesA+B < #SitesA + #SitesB
The Shared Network should always provide a better network customer experience than each standalone networks.
I have experienced Executives argue (usually post-deal obviously!) that it is not possible to remove sites, as any site removed will destroy customer experience. Let me be clear, If the shared network is planned & optimized according with best practices the shared network will deliver a substantial better network experience to the combined customer base than the respective standalone networks.
Lets dive deeper into the Technology Cost Structure. As the Figure below shows (i.e., typical for mature western markets) we have the following high level cost distribution for the Technology Opex
- 10% to 15% for Core Network
- 20% to 40% for IT & Platforms and finally
- 45% to 70% for RAN.
The RAN Opex for markets without energy distribution challenges, i.e., mature & reliable energy delivery grid) is split in (a) ca. 40% (i.e., of the RAN Opex) for Rental & Leasing which is clearly addressable by Network Sharing, (b) ca. 25% in Services including Maintenance & Repair of which at least the non-Telco part is easily addressable by Network Sharing, (c) ca. 15% Personnel Cost also addressable by Network Sharing, (d) 10% Leased Lines (typical backhaul connectivity) is less dependent on Network Sharing although bandwidth volume discounts might be achievable by sharing connectivity to a shared site and finally (e) Energy & other Opex costs would in general not be impacted substantially by Network Sharing. Note that for markets with a high share of diesel generators and fuel logistics, the share of Energy cost within the RAN Opex cost category will be substantially larger than depicted here.
It is important to note here that sharing of Managed Energy Provision, similar to Tower Company lease arrangement, might provide financial synergies. However, typically one would expect Capex Avoidance (i.e., by not buying power systems) on the account of an increased Energy Opex Cost (compared to standalone energy management) for the managed services. Obviously, if such a power managed service arrangement can be shared, there might be some synergies to be gained from such an arrangement. In my opinion this is particular interesting for markets with a high reliance of diesel generators and fuelling logistics.This said
Power sharing in mature markets with high electrification rates can offer synergies on energy via applicable volume discounts though would require shared metering (which might not always be particular well appreciated by power companies).
Maybe as much as
80% of the total RAN Opex can be positively impacted (i.e., reduced) by network sharing.
Above cost structure illustration also explain why I rarely get very exited about sharing measures in Core Network Domain (i.e., spend too much time in the past to explain that while NG Core Network might save 50% of relevant cost it really was not very impressive in absolute terms and efforts was better spend on more substantial cost structure elements). Assume you can save 50% (which is a bit on the wild side today) on Core Network Opex (even Capex is in proportion to RAN fairly smallish). That 50% saving on Core translates into maybe maximum 5% of the Network Opex as opposed to RAN’s 15% – 20%. Sharing Core Network resources with another party does require substantially more overhead management and supervision than even fairly aggressive RAN sharing scenarios (with substantial active sharing).
This said, I believe that there are some internal efficiency measures to Telco Groups (with superior interconnection) and very interesting new business models out there that do provide core network & computing infrastructure as a service to Telco’s (and in principle allow multiple Telco’s to share the core network platforms and resources. My 2012 presentation on Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. illustrates how such business models might work out. The first describes in largely generic terms how virtualization (e.g., NFV) and cloud-based technologies could be exploited. The LTE-as-a-Service (could be UMTS-as-a-Service as well of course) is more operator specific. The verdict is still out there whether truly new business models can provide meaningful economics for customer networks and business. In the longer run, I am fairly convinced, that scale and expected massive improvements in connectivity in-countries and between-countries will make these business models economical interesting for many tier-2, tier-3 and Generation-Z businesses.
BUT BUT … WHAT ABOUT CAPEX?
From a Network Sharing perspective Capex synergies or Capex avoidance are particular interesting at the beginning of a network rollout (i.e., Rollout Phase) as well as at the end of the Steady State where technology refreshment is required (i.e., the Modernization Phase).
Obviously, in a site deployment heavy scenario (e.g., start-ups) sharing the materials and construction cost of greenfield tower or rooftop (in as much as it can be shared) will dramatically lower the capital cost of deployment. In particular as you and your competitor(s) would likely want to cover pretty much the same places and thus sharing does become very compelling and a rational choice. Unless its more attractive to block your competitor from gaining access to interesting locations.
Irrespective, between 40% to 50% of an operators sites will only generate up-to 10% of the turnover. Those ugly-cost-tail sites will typically be in rural areas (including forests) and also on average be more costly to deploy and operate than sites in urban areas and along major roads.
Sharing 40% – 50% of sites, also known as the ugly-cost-tail sites, should really be a no brainer!
Depending on the market, the country particulars, and whether we look at emerging or mature markets there might be more or less Tower sites versus rooftops. Rooftops are less obvious passive sharing candidates, while Towers obviously are almost perfect passive sharing candidates provided the linked budget for the coverage can be maintained post-sharing. Active sharing does make rooftop sharing more interesting and might reduce the tower design specifications and thus optimize Capex further in a deployment scenario.
As operators faces RAN modernization pressures it can Capex-wise become very interesting to discuss active as well as passive sharing with a competitor in the same situation. There are joint-procurement benefits to be gained as well as site consolidation scenarios that will offer better long-term Opex trends. Particular T-Mobile and Hutchinson in the UK (and T-Mobile and Orange as well in UK and beyond) have championed this approach reporting very substantial sourcing Capex synergies by sharing procurements. Note network sharing and sharing sourcing in a modernization scenario does not force operators to engage in full active network sharing. However, it is a pre-requisite that there is an agreement on the infrastructure supplier(s).
Network Sharing triggered by modernization requirements is primarily interesting (again Capex wise) if part of electronics and ancillary can be shared (i.e., active sharing). Suppliers match is an obviously must for optimum benefits. Otherwise the economical benefits will be weighted towards Opex if a sizable amount of sites can be phased out as a result of site consolidation.
The above Figure provides an overview of the most interesting components of Network Sharing. It should be noted that Capex prevention is in particular relevant to (1) The Rollout Phase and (2) The Modernization Phase. Opex prevention is always applicable throughout the main 3 stages Network Sharing Attractiveness Cycles. In general the Regulatory Complexity tend to be higher for Active Sharing Scenarios and less problematic for Passive Sharing Scenarios. In general Regulatory Authorities would (or should) encourage & incentivize passive site sharing ensuring that an optimum site infrastructure (i.e., number of towers & rooftops) is being built out (in greenfield markets) or consolidated (in established / mature markets). Even today it is not un-usual to find several towers, each occupied with a single operator, next to each other or within hundred of meters distance.
NETWORK SHARING DOES NOT COME FOR FREE!
One of the first things a responsible executive should ask when faced with the wonderful promises of network sharing synergies in form of Ebitda and cash improvements is
What does it cost me to network share?
The amount of re-structuring or termination cost that will be incurred before Network Sharing benefits can be realized will depend a lot on which part of the Network Sharing Cycle.
(1) The Rollout Phase in which case re-structuring cost is likely to be minimum as there is little or nothing to restructure. Further, also in this case write-off of existing investments and assets would likewise be very small or non-existent pending on how far into the rollout the business would be. What might complicate matters are whether sourcing contracts needs to be changed or cancelled and thus result in possible penalty costs. In any event being able to deploy together the network from the beginning does (in theory) result in the least deployment complexity and best deployment economics. However, getting to the point of agreeing to shared deployment (i.e., which also requires a reasonable common site grid) might be a long and bumpy road. Ultimately, launch timing will be critical to whether two operators can agree on all the bits and pieces in time not to endanger targeted launch.
Network Sharing in the Rollout Phase is characterized by
(2) The Steady State Phase, where a substantial part of the networks have been rollout out, tend to be the most complex and costly phase to engage in Network Sharing passive and of course active sharing. A substantial amount of site leases would need to be broken, terminated or re-structured to allow for network sharing. In all cases either penalties or lease increases are likely to result. Infrastructure supplier contracts, typically maintenance & operations agreements, might likewise be terminated or changed substantially. Same holds for leased transmission. Write-off can be very substantial in this phase as relative new sites might be terminated, new radio equipment might become redundant or phased-out, etc If one or both sharing partners are in this phase of the business & network cycle the chance of a network sharing agreement is low. However, if a substantial amount of both parties site locations will be used to enhance the resulting network and a substantial part of the active equipment will be re-used and contracts expanded then sharing tends to be going ahead. A good example of this is in the UK with Vodafone and O2 site sharing agreement with the aim to leapfrog number of sites to match that of EE (Orange + T-Mobile UK JV) for improved customer experience and remain competitive with the EE network.
Network Sharing in the Steady State Phase is characterized by
(3) Once operators approaches the Modernization Phase more aggressive network sharing scenarios can be considered as the including joint sourcing and infrastructure procurement (e.g., a la T-Mobile UK and Hutchinson in UK). At this stage typically the remainder of the site leases term will be lower and penalties due to lease termination as a result lower as well. Furthermore, at this point in time little (or at least substantially lower than in the steady state phase) residual value should remain in the active and also passive infrastructure. The Modernization Phase is a very opportune moment to consider network sharing, passive as well as active, resulting in both substantial Capex avoidance and of course very attractive Opex savings mitigating a stagnating or declining topline as well as de-risking future loss of profitability.
Network Sharing in the Modernization Phase is characterized by
- Relative moderate restructuring & termination cost expected.
- High Capex avoidance potential.
- Substantial Opex saving potential.
- Little infrastructure write-offs.
- Lower risk of contract termination penalties.
- Manageable consolidation project.
- Instant cell splits and cost-efficient provision of network capacity.
- More aggressive network optimization –> better network.
As a rule of thumb I usually recommend to estimate restructuring / termination cost as follows (i.e., if you don’t have the real terms & conditions of contracts by the hand);
- 1.5 to 3+ times the estimated Opex savings – use the higher multiple in the Steady State Phase and the Lower for Modernization Phase.
- Consolidation Capex will often be partly synergetic with Business-as-Usual (BaU) Capex and should not be fully considered (typically between 25% to 50% of consolidation Capex can be mapped to BaU Capex).
- Write-offs should be considered and will be the most pain-full to cope with in the Steady State Phase.
NATIONAL ROAMING AS AN ALTERNATIVE TO NETWORK SHARING.
A National Roaming agreement will save network investments and the resulting technology Opex. So in terms of avoiding technology cost that’s an easy one. Of course from a Profit & Loss (P&L) perspective I am replacing my technology Opex and Capex with wholesale cost somewhere else in my P&L. Whether National Roaming is attractive or not will depend a lot of anticipated traffic and of course the wholesale rate the hosting network will charge for the national roaming service. Hutchinson in UK (as well in other markets) had for many years a GSM national roaming agreement with Orange UK, that allowed its customers basic services outside its UMTS coverage footprint. In Austria for example, Hutchinson (i.e., 3 Austria) provide their customers with GSM national roaming services on T-Mobile Austria’s 2G network (i.e., where 3 Austria don’t cover with their own 3G) and T-Mobile Austria has 3G national roaming arrangement with Hutchinson in areas that they do not cover with 3G.
In my opinion whether national roaming make sense or not really boils down to 3 major considerations for both parties:
There are plenty of examples on National Roaming which in principle can provide similar benefits to infrastructure sharing by avoidance of Capex & Opex that is being replaced by the cost associated with the traffic on the hosting network.The Hosting MNO gets wholesale revenue from the national roaming traffic which the Host supports in low-traffic areas or on a under-utilized network. National roaming agreements or relationships tends to be of temporary nature.
It should be noted that National Roaming is defined in an area were 1-Party The Host has network coverage (with excess capacity) and another operator (i.e., The Roamer or The Guest) has no network coverage but has a desire to offer its customers service in that particular area. In general only the host’s HPLMN is been broadcasted on the national roaming network. However, with Multi-Operator Core Network (MOCN) feature it is possible to present the national roamer with the experience of his own network provided the roamers terminal equipment supports MOCN (i.e., Release 8 & later terminal equipment will support this feature).
In many Network Sharing scenarios both parties have existing and overlapping networks and would like to consolidate their networks to one shared network without loosing service quality. The reduction in site locations provide the economical benefits of network sharing. Throughout the shared network both operators will radiate their respective HPLMNs and the shared network will be completely transparent to their respective customer bases.
While having been part of several discussions to shut down one networks in geographical areas of a market and move customers to a host overlapping (or better) network via a national roaming agreement, I am not aware of mobile operators which have actually gone down this path.
Regulatory and from a spectrum safeguard perspective it might be a better approach to commission both parties frequencies on the same network infrastructure and make use of for example the MOCN feature that allows full customer transparency (at least for Release 8 and later terminals).
National Roaming is fully standardized and a well proven arrangement in many markets around the world. One does need to be a bit careful with how the national roaming areas are defined/implemented and also how customers move back and forth from a national roaming area (and technology) to home area (and technology). I have seen national roaming arrangements not being implemented because the dynamics was too complex to manage. The “cleaner” the national roaming area is the simpler does the on-off national roaming dynamics become. With “Clean” is mean keep the number of boundaries between own and national roaming network low, go for contiguous areas rather than many islands, avoid different technology coverage overlap (i.e., area with GSM coverage, it should avoided to do UMTS national roaming), etc.. Note you can engineer a “dirty” national roaming scenario of course. However, those tend to be fairly complex and customer experience management tends to be sub-optimal.
Network Sharing and National Roaming are from a P&L perspective pretty similar in the efficiency and savings potentials. The biggest difference really is in the Usage Based cost item where a National Roaming would incur higher cost than compared to a Network Sharing arrangement.
An Example: Operator contemplate 2 scenarios;
- Network Sharing in rural area addressing 500 sites.
- Terminate 500 sites in rural area and make use of National Roaming Agreement.
What we are really interested in, is to understand when Network Sharing provides better economics than National Roaming and of course vice versa.
National Roaming can be attractive for relative low traffic scenarios or in case were product of traffic units and national roaming unit cost remains manageable and lower than the Shared Network Cost.
The above illustration ignores the write-off and termination charges that might result from terminating a given number of sites in a region and then migrate traffic to a national roaming network (note I have not seen any examples of such scenarios in my studies).
The termination cost or restructuring cost, including write-off of existing telecom assets (i.e., radio nodes, passive site solutions, transmission, aggregation nodes, etc….) is likely to be a substantially financial burden to National Roaming Business Case in an area with existing telecom infrastructure. Certainly above and beyond that of a Network Sharing scenario where assets are being re-used and restructuring cost might be partially shared between the sharing partners.
Obviously, if National Roaming is established in an area that has no network coverage, restructuring and termination cost is not an issue and Network TCO will clearly be avoided, Albeit the above economical logic and P&L trade-offs on cost still applies.
National Roaming can be an interesting economical alternative, at least temporarily, to Network Sharing or establishing new coverage in an area with established network operators.
However, National Roaming agreements are usually of temporary nature as establishing own coverage either standalone or via Network Sharing eventually will be a better economical and strategic choice than continuing with the national roaming agreement.
SHARING BY TOWER COMPANY (TOWERCO).
There is a school of thought, within the Telecommunications Industry, that very much promotes the idea of relying on Tower Companies (Towerco) to provide and manage passive telecom site infrastructure.
The mobile operator leases space from the Towerco on the tower (or in some instances a rooftop) for antennas, radio units and possible microwave dishes. Also the lease would include some real estate space around the tower site location for the telecom racks and ancillary equipment.
In the last 10 years many operators have sold off their tower assets to Tower companies that then lease those back to the mobile operator.
In most Towerco deals, Mobile Operators are trading off up-front cash for long-term lease commitments.
With the danger of generalizing, Towerco deals made by operators in my opinion have a bit the nature and philosophy of “The little boy peeing in his trousers on a cold winter day, it will warm him for a short while, in the long run he will freeze much more after the act”. Let us also be clear that the business down the road will not care about a brilliant tower deal (done in the past) if it pressures their Ebitda and Site Lease cost.
In general the Tower company will try (should be incented) to increase the tower tenancy (i.e., having more tenants per tower). Pending on the lease contract the Towerco might (should!) provide the mobile operator lease discount as more tenants are added to a given tower infrastructure.
Towerco versus Network Sharing is obviously a Opex versus Capex trade-off. Anyway, lets look at a simple total-cost-of-ownership example that allows us to understand better when one strategy could be better than the other.
From the above very simple and high level per tower total-cost-of-ownership model its clear that a Towerco would have some challenges in matching the economics of the Shared Network. A Mobile Operator would most likely (in above example) be better of commencing on a simple tower sharing model (assuming a sharing partner is available and not engaging with another Towerco) rather than leasing towers from a Towerco. The above economics is ca. 600 US$ TCO per month (2-sharing scenario) compared to ca. 1,100 (2-tenant scenario). Actually, unless the Towerco is able to (a) increase occupancy beyond 2, (b) reduce its productions cost well below what the mobile operators would be (without sacrificing quality too much), and (c) at a sufficient low margin, it is difficult to see how a Towerco can provide a Tower solution at better economics than conventional network shared tower.
This said it should also be clear that the devil will be in the details and there are various P&L and financial engineering options available to mobile operators and Towercos that will improve on the Towerco model. In terms of discounted cash flow and NPV analysis of the cash flows over the full useful life period the Network Sharing model (2-parties) and Towerco lease model with 2-tenants can be made fairly similar in terms of value. However, for 2-tenant versus 2-party sharing, the Ebitda tends to be in favor of network sharing.
For the Mobile Network Operator (MNO) it is a question of committing Capital upfront versus an increased lease payment over a longer period of time. Obviously the cost of capital factors in here and the inherent business model risk. The inherent risk factors for the Towerco needs to be considered in its WACC (weighted average cost of capital) and of course the overall business model exposure to
- Operator business failure or consolidation.
- Future Network Sharing and subsequent lease termination.
- Tenant occupancy remains low.
- Contract penalties for Towerco non-performance, etc..
Given the fairly large inherent risk (to Towerco business models) of operator consolidation in mature markets, with more than 3 mobile operators, there would be a “wicked” logic in trying to mitigate consolidation scenarios with costly breakaway clauses and higher margins.
From all the above it should be evident that for mobile operators with considerable tower portfolios and also sharing ambitions, it is far better to (First) Consolidate & optimize their tower portfolios, ensuring minimum 2 tenants on each tower and then (Second) spin-off (when the cash is really needed) the optimized tower portfolio to a Towerco ensuring that the long-term lease is tenant & Ebitda optimized (as that really is going to be any mobile operations biggest longer term headache as markets starts to saturate).
SUMMARY OF PART I – THE FUNDAMENTALS.
There should be little doubt that
Network Sharing provides one of the biggest financial efficiency levers available to mobile network operator.
Maybe apart from reducing market invest… but that is obviously not really a sustainable medium-long-term strategy.
In aggressive network sharing scenarios Opex savings in the order of 35% is achievable as well as future Opex avoidance in the run-rate. Depending on the Network Sharing Scenario substantial Capex can be avoided by sharing the infrastructure built-out (i.e., The Rollout Phase) and likewise in the Modernization Phase. Both allows for very comprehensive sharing of both passive and active infrastructure and the associated capital expenses.
Both National Roaming and Sharing via Towerco can be interesting concepts and if engineered well (particular financially) can provide similar benefits as sharing (active as well as passive, respectively). Particular in cash constrained scenarios (or where operators see an extraordinary business risk and want to minimize cash exposure) both options can be attractive. Long-term National Roaming is particular attractive in areas where an operator have no coverage and has little strategic importance. In case an area is strategically important, national roaming can act as a time-bridge until presence has been secure possibly via Network Sharing (if competitor is willing).
Sharing via Towerco can also be an option when two parties are having trust issues. Having a 3rd party facilitating the sharing is then an option.
In my opinion National Roaming & Sharing via Towerco rarely as Ebitda efficient as conventional Network Sharing.
Finally! Why should you stay away from Network Sharing?
This question is important to answer as well as why you should (which always seems initially the easiest). Either to indeed NOT to go down the path of network sharing or at the very least ensure that point of concerns and possible blocking points have been though roughly considered and checked of.
So here comes some of my favorites … too many of those below you are not terrible likely to be successful in this endeavor:
I would like to thank many colleagues for support and Network Sharing discussions over the past 13 years. However, in particular I owe a lot to David Haszeldine (Deutsche Telekom) for his insights and thoughts. David has been my true brother-in-arms throughout my Deutsche Telekom years and on our many Network Sharing experiences we have had around the world. I have had many & great discussions with David on the ins-and-outs of Network Sharing … Not sure we cracked it all? … but pretty sure we are at the forefront of understanding what Network Sharing can be and also what it most definitely cannot do for a Mobile Operator. Of course similar to all the people who have left comments on my public presentations and gotten in contact with me on this very exiting and by no way near exhausted topic of how to share networks.
The term the “Ugly Tail” as referring to rural and low-profitability sites present in all networks should really be attributed to Fergal Kelly (now CTO of Vodafone Ireland) from a meeting quiet a few years ago. The term is too good not to borrow … Thanks Fergal!
This story is PART I and as such it obviously would indicate that another Part is on the way … PART II – “Network Sharing – That was then, this is now” will be on the many projects I have worked on in my professional career and lessons learned (all available in the public domain of course). Here obviously providing a comparison with the original ambition level and plans with the reality is going to be cool (and in some instances painful as well). PART III – “The Tools” will describe the arsenal of tools and models that I have developed over the last 13 years and used extensively on many projects.
- A Billion GSM subscriptions & almost $200 Billion GSM revenue will have gone within the next 5 years.
- GSM earns a lot less than its “fair” share of the top-line, a trend that will further worsened going forward.
- GSM revenue are fading out rapidly across a majority of the mobile markets across the Globe.
- Accelerated GSM phase-out happens when pricing level of the next technology option relative to the GDP per capita drops below 2%.
- 220 MHz of great spectrum is tied up in GSM, just waiting to be liberated.
- GSM is horrific spectral in-efficient in comparison to today’s cellular standards.
- Eventually we will have 1 GSM network across a given market, shared by all operators, supporting fringe legacy devices (e.g., M2M) while allowing operators to re-purpose remaining legacy GSM spectrum.
- The single Shared-GSM network might survive past any economical justification for its existence merely serving legal and political interests.
Gone So Much … GSM is ancient, uncool and so 90s … why would anybody bother with that stuff any longer … its synonymous with the Nokia Handset (which btw is also ancient, uncool and so 90s … and almost no longer among us thanks to our friend Elop …). In many emerging markets GSM-only phones are hardly demanded or sold any longer in the grey markets. Grey market that make up 90% (or more) of handset sales in many of those emerging markets. Moreover, its not only AT&T in the US talking about 2G phase-out but also an emerging market such as Thailand is believed to be void of GSM within the next couple of years.
A bit of Personal History. Some years ago I had the privilege to work with some very smart people in the Telecom Industry on merging two very big mobile operations (ca.140 million in combined customer base). One of our cardinal spectrum strategic and technology arguments were the gain in spectral efficiency such a merger would bring. Anecdotally it is worth mentioning that the technology synergies and spectrum strategic ideas largely would have financed the deal in shear synergies.
In discussions with the country’s regulator we were asked why we could not “just” switch off GSM? Then use that freed GSM spectrum for new cellular technologies, such as UMTS and even LTE. Thereby gaining sufficient spectral efficiency that merging the two business would become un-necessary. The proposal would have effectively turned off the button of a service that served at ca. 70 Million GSM-only (incl, EDGE & GPRS) subscribers (at the time) across the country. Now that would have been expensive and most likely caused a couple or thousands of class action suits to the beat.
Here is how one could have thought about the process of clearing out GSM for something better (though overall its is more for richer and poorer). There is no “just …press the off button”, as also Sprint experienced with their iDEN migration.
Our thoughts (and submitted Declarations) were that by merging the two operators spectrum (and sites pool) we could create sufficient spectral capacity to support both GSM (which we all granted was phasing out) and provide more capacity and customer experience for the Now Generation Technology (i.e., HSPA+ or 4G as they like to call it in that particular market … Heretics! ;-). A recent must read GigaOM blog by Keith Fitchard “AT&T begins cannibalizing 2G and 3G networks to boost LTE capacity” describes very well the aggressive no-nonsense thinking of US carriers (or simply desperation or both) when it comes to the quest for spectrum efficiency and enhanced customer experience (which co-incidentally also yields the best ARPUs).
It is worth mentioning that more than 2×110 Mega Hertz is tied up in GSM, Up-to 2×35 MHz at 900MHz (if E-GSM has been evoked) and 2×75 MHz at 1800MHz (yes! I am ignoring US GSM band plans, they are messed up but pretty fun nevertheless … different story for another time). Being able to re-purpose this amount of spectrum to more spectral efficient cellular technologies (e.g., UMTS Voice, HSPA+ and LTE) would clearly leapfrog mobile broadband, increase voice capacity at increased quality, and serve the current billions of GSM-only users as well as the next billion un-connected or under-server customer segments with The Internet. The macro-economical benefits being very substantial.
220 MHz of great spectrum is tied up in GSM, just waiting to be liberated.
Back in the days of 2003 I did my first detailed GSM phase-out techno-economical analysis (a bit premature one might add). I was very interested in questions such as “when can we switched off GSM?”, “what are the economical premises of exiting GSM?”, “Why do operators today still continue to encourage subscriber growth on their GSM networks?”, “Today … if you got your hands on GSM usable spectrum, would you start a GSM operation?”, “Why?” and “Why not?”, etc..
So why don’t we “just” switch off GSM? and let go of that old in-efficient cellular technology?
How in-efficient? you may ask? … Pending a little bit on what state the GSM is in, we can have ca. 3 times more voice users in WCDMA (i.e., UMTS) compared to GSM with Adaptive Multi-Rate (AMR) codec support. Newer technology releases supports even more dramatic leaps in voice handling capabilities.
Data? what about cellular data? That GSM, including its data handling enhancements GPRS and EDGE, is light-bits away from the data handling capabilities of WCDMA, HSPA+, LTE and so forth is at this point a well establish fact.
Clearly GSM is horrific spectral in-efficient in comparison to later cellular standards such as WCDMA, HSPA(+) and LTE(+) and its only light (in a very dark tunnel) is that it is supported at lower frequencies (i.e., more economical deployment in rural areas and for large surface area countries). Though today that no longer unique as UMTS and LTE are available in similar or even lower frequency ranges. … of course there are other economical issues at plays as well, which we will see below.
Why do we still bother with a 27+ year old technology? a technology that has very poor spectral efficiency in comparison with later cellular technologies. GSM after all “only” provides Voice, SMS and pretty low bandwidth mobile data (while better than nothing, still very close to nothing).
Well for one thing! there is of course the money thing? (and we know that that makes the world go around) ca. 4+ Billion GSM subscriptions worldwide (incl. GPRS & EDGE) generating a total GSM turnover of 280+ Billion US$.
In 2017 we anticipate to have a little less than 3 Billion GSM subscriptions generating ca. 100+ Billion US$. So ….a Billion GSM subscriptions and almost 200 Billion US$ GSM revenue will have dis-appeared within the next 5 years (and for the sake of mobile operators hopefully replaced by something better).
In this trend APAC, takes its lion share of the GSM subscription loss with ca. 65% (ca, 800 Million) of the total loss and ca. 50% of the GSM top-line loss (ca. 100 Billion US$).
The share of GSM revenue is rapidly declining across (almost) all markets;
The GSM revenue as share of the total revenue (as well as in absolute terms) rapidly diminishes, as 3G and LTE are introduced and customer migrate to those more modern technologies.
If the should be any doubts GSM does not get its fair share revenue compared to its share of the subscriptions (or subscribers for that matter):
While the above data does contain two main clusters, it still pretty well illustrates (what should be no real surprise to any one) that GSM earns back a lot less than its “fair” share (whatever that really means). And again if anyone would be in doubt that picture will be grimmer as the we fast forward to the near future;
Grim, Grimmer, Grimmest!
Today GSM earns a lot less than its “fair” share of the top-line, this trend will be further worsened going forward.
So we can soon phase-out GSM? Right? hmmmm! Maybe not so fast!
Well while GSM revenue has certainly declined and expected to continue the decline, in many markets the GSM-only (e.g., here defined as a customers that only have GSM Voice, GPRS and/or EDGE available) customers have not declined in proportion to the related revenue might fool us to believe.
The above statistics illustrates the GSM-only subscription share of the total cellular business.
The GSM revenue are expected to massively free fall over the next 5 years!
However, also observe (in the chart above) that we need to sustain the network and its associated cost as a considerable amount of customers remain on the network, despite generating a lot less top line.
As we have already seen above, in the next 5 years there will be many markets where GSM subscription and subscriber share will remain reasonable strong albeit the technology’s ability to turn-over revenue will be in free-fall in most markets.
Analyzing data from Pyramid Research (actual & projection for the period 2013 to 2017), including other analyst data sets (particular on actual data), extrapolating the data beyond 2017 by diffusion models approximating the dynamics of technology migration in the various market, we can get an idea about the remaining (residual) life of GSM. In other words we can make GSM phase-out projections as well as get a feel for the terminal revenue (or residual value) left in GSM. Further get an appreciation of how that terminal value compares to the total mobile turnover over the same GSM phase-out period.
The chart below provide the results of such a comprehensive analysis. The colored bars illustrate the various years of onset of GSM phase-out; (a) the earliest year which is equal to the lower end of light-blue bar is typically the year where migration off GSM accelerates, (b) the upper end of the light-blue bar is a most-likely year where after GSM no longer would be profitable, and (c) the upper end of the red bar illustrates the maximum expected life of GSM. It should be noted that the GSM Phase-out chart below might not be shown in its entirety (particular right side of the chart). Clicking on the Chart itself will display it in full.
Taking the above GSM phase-out years, we can get a feeling for how many useful years GSM has left in terms of economical-life and customer life-time defined as which event comes first of (i) less than 1 Million GSM subscriptions or (ii) 5% GSM market-share. 2014 has been taken as the reference year;
It should be noted that the Useful Life-span of GSM chart above might not be shown in its entirety (particular right side of the chart). Clicking on the Chart itself will display it in full.
|Western Europe||16||4.1 +/- 3.3 years|
|Asia Pacific||13||6.4 +/- 5.0 years|
|Middle East & Africa||17||11.0 +/- 6.2 years|
|Central Eastern Europe||8||6.9 +/- 4.8 years|
|Latin America||19||6.6 +/- 3.7 years|
That Western Europe (and US which has not been shown here) has the most aggressive time-lines for GSM phase-out should come as no surprise. The 3G/UMTS has been deployed there the longest and the 3G price level to GDP has come down to a level where there is hardly any barrier for most mobile users to switch from GSM to UMTS. Also the WEU region has the most extensive UMTS coverage which also removes the GSM to UMTS switching barrier. Central Eastern Europe average is pulled up (i.e., longer useful life) substantial by Russia and Ukraine that shows fairly extreme laggardness in GSM phase-out (in comparison with the other CEE markets). For Middle East and Africa it should be noted that there are two very strong clusters of data distinguishing the Gulf States from the African Countries. Most of the Gulf States have only a very few years of remaining useful life of GSM. In general the GSM remaining life trend can be described fairly well with the amount of time UMTS has been in a given market (i.e., though smartphone introduction did kick-start the migration from GSM more than anything else), the extend of UMTS coverage (i.e., degree of pop and geo coverage) and the basic economics of UMTS.
In my analysis I have assumed 4 major triggers for GSM phase-out;
- Analysis shows that once the 3G (or non-2G) ARPU is below 2% of the nominal GDP per capita an acceleration of migration away from GSM speeds up. I have (somewhat arbitrarily) chosen 1% as my limit where there is no longer any essential barrier of customer migrating off GSM.
- When GSM penetration is below 5% as a decision point for converting (by possible subsidies) GSM customers to a more modern and efficient technology. This obviously does depend on total customer base and the local economical framework and as such is only a heuristics rather than a universal rule.
- Last but not least, my 3rd criteria for phasing out GSM is when its base is below 1 million subscriptions (i.e., typically 500 to 800 thousand subscribers).
- Last but not least, before complete phase-out of GSM can commence, operators obviously need to provide the alternative technology (e.g., UMTS or LTE) coverage that can replace the existing GSM coverage. This is in general only economical if comparable frequency range can be used and thus for example for UMTS coverage replacement of GSM in many cases re-farming/re-purposing 900MHz from GSM to UMTS. This last point can be a very substantial bottleneck and show stopper for migration from GSM to UMTS, particular in rural areas or in countries with very substantial rural populations on GSM.
Interestingly enough, extensive data analysis on more than 70 markets, shows that the GSM phase—out dynamics appears to have little or no dependency on (a) the 2G ARPU level, (b) 2G ARPU level relative to 3G ARPU and (c) handset pricing (although I should point out that I have not had a lot of data here to be firm in this conclusion, in particular reliable data for grey market handset pricing across the emerging markets is a challenge).
One of the important trigger points for onset of accelerated GSM phase-out is the pricing level of the next technology (e.g., 3G) option relative to the GDP per capita.
Migration decision appears less to do with the legacy price of the old technology or old technology price relative to new technology pricing.
Above chart illustrates an analysis made on 2012 actual data for more than 70+ markets all across WEU, CEE, APAC, EMEA and LA (i.e., coinciding with markets covered by Pyramid Research). It is very interesting to observe the dynamics as the markets develop into the future and the data moves towards the left indicating more affordable 3G pricing (relative to GDP per capita) and increasingly faster GSM phase-out as is evident from the chart below providing the same markets as above but fast forwarded 5 years (i.e., 2017).
Firstly the GSM ARPU level across most markets is below 2% of a given markets GDP per capita. There is no clear evidence in the country data available that the GSM ARPU development has had any effect on slowing down or accelerating GSM phase-out. Most likely an indication that GSM has reached (or will reach shortly) a cost level where customers become insensitive.
Conceptually we can visualize the GSM phase-out dynamics in the following way were as the 3G gets increasingly affordable (which may or should include the device cost depending on taste), GSM phase-out accelerates (i.e., moving from right to left in the illustrative chart below). While the chart illustration below is more attuned to emerging market migration dynamics of GSM phase-out it can of course with minor adaptations be used for other more balanced prepaid-postpaid markets.
We should keep in mind that unless the mobile operators new technology coverage (e.g., UMTS, LTE, ..) at the very least overlap the GSM coverage, the migration from GSM to UMTS (or LTE) will eventually stop. This can in countries with a substantial rural population in particular become a blocking stone for an effective 100% migration. Resulting in large areas and population share that will remain underserved (i.e., only GSM available) and thus depend on an in-efficient and ancient technology without the macro-economical benefits (i.e., boost of rural GDP) new and far more efficient cellular technologies could bring.
That’s all fine … what a surprise that customers wants better when it gets affordable (like to have wanted that even more when it was not affordable)… and that affordability is relative is hardly a surprising either.
In order for an operator to make an informed opinion about when to switch off GSM, it would need to evaluated the remaining business opportunity, or residual GSM value, against the value for re-purposing the GSM spectrum to a better technology, i.e., with a superior customer experience potential, and with a substantial higher ARPU utilization.
Counting from 2014, the remaining life-time aka terminal aka residual GSM revenue will be in the order of 850 Billion US$ … agreeable an apparently dramatic number … however, the residual GSM revenue is on average no more than 5% of total cellular turnover and for many countries a lower than that. Actual 45 markets out of the 73 studied will have a terminal GSM revenue lower than 5%.
The chart below provides an overview of the Residual GSM Revenues in Billion of US$ (on a logarithmic scale) and the percentage of Residual GSM value out of the total cellular turnover (linear scale) for 75 top markets spread across Western Europe, Central Eastern Europe, Asia Pacific, Middle East & Africa, and Latin America.
Do note that the GSM Terminal Revenue chart above might not be shown in its entirety (right side of the chart). Clicking on the Chart itself will display it in full.
It is quiet clear from the above chart that, apart from a few outliers, GSM revenue are fading out rapidly across a majority of the mobile markets across the globe. Even if the residual GSM topline might appear tempting, it obviously need to be compared to the operating expenses for sustaining the legacy technology as well as considering that a more modern technology would create higher efficiency (and possible ARPU arbitrage) and therefor mitigate margin decline sustaining more traffic and customers.
Emerging APAC MNO Example: an emerging market in APAC has 100 Million subscriptions and ca. 70 Million unique cellular user base.One of the Mobile Network Operators (MNO) in this market has approx. 33% market share (revenue share slightly larger). in 2012 its EBITDA margin was 42%. Technology cost share of overall Opex is 25% and for the sake of simplicity the corresponding GSM cost share is in 2012 assumed to be 50% of the Total Technology Opex. As the business evolves it is assumed that the GSM cost base grows slower than non-GSM technology cost elements. This particular market has a residual GSM revenue potential of approx. 4 Billion US$ and the MNO under the loop has 1.3 Billion US$ remaining GSM revenue potential.
Our analysis shows that the GSM business would start to breakdown (within the assumed economical framework or template) at around 5 Million GSM subscriptions or 3.5 Million unique users. This would happen around 2019 (+/- 2 years, with a bias towards earlier years) and thus leave the business with another 3 to 5 years of likely profitable GSM operation. See the chart below.
This illustration shows (not surprisingly) that there is a point where even if the phasing-out GSM turns-over revenue, from an economical perspective it makes no sense for a single mobile operator to keep its GSM network alive for a diminishing customer base and even faster evaporating top-line.
In the example above it is clear that the MNO should start planning for the inevitable – the demise of GSM. Having a clear GSM phase-out strategy as soon as possible and targeting GSM termination no later than 2018 to 2019 just makes pretty good sense. Looking at risks to the dynamics of the market development in this particular market there is a higher likelihood of no-profit being reached earlier rather than later.
Would it make sense to startup a new GSM business in the market above? Given the 3 to 5 years that the existing mobile operators have to meet retire GSM before it becomes un-profitable, it hardly make much sense for a Greenfield operator to get started on the GSM idea (seem to be better ways for spending cash).
However, if that Greenfield operator could become The GSM Operator for all existing MNO players in the market, allowing those legacy MNOs to re-purpose their existing GSM spectrum (and possible with a retro-active wholesale deal), then maybe in the short term it might make a little sense. However, it quiet frankly would be like peeing in your trousers on a cold winter day, it will be warm for a short while but then it really gets cold (as my Grandmother used to say).
What GSM strategies makes really sense in its autumn days?
Quit clearly GSM Network Sharing would make a lot of sense economically and operationally as it would allow re-purposing of legacy spectrum to more modern and substantially more efficient cellular technologies.
The single Shared-GSM network would act as a bridge for legacy GSM M2M devices, extreme laggards and problematic coverage areas that might not be economical to replace in the shorter – medium term. Thus mobile operators could then solve possible long-term contractual obligations to businesses and consumers having fringe devices connecting with GSM (i.e., metering, alarms, etc..). The single Shared-GSM network might very well survive for a considerable time past any economical justification for its existence merely serving legal and political interests. Thanks to Stein Erik Paulsen who pointed this problem out for GSM phase-out.
I am not (too) hanged up about the general Capex & Opex benefits of Network Sharing in this context (yet another story for another day). The compelling logical step of having 1 (ONE) GSM network across a given market, shared by all operators, supporting the phase-out of GSM while allowing to re-purpose legacy GSM spectrum for UMTS/HSPA and eventually LTE(+), is almost screamingly obvious. This furthermore would feed a faster migration pace and phase-out as legacy spectrum would be available for re-purposing and customer migration.
Of course Regulatory authorities would need to endorse such a scenario as it de-facto would result in a smelling-like creating a monopolistic GSM operator albeit serving all in a given market.
The Regulatory Authority should obviously be very interested in this strategy as it would ensure substantial better utilization of scarce spectral resources. Furthermore, not only gaining in spectral efficiency but also winning the macro-economical boost from connecting the unconnected and under-served population groups to mobile data networks, and by that, the internet.
I have made extensive use of historical and actual data from Pyramid Research country data bases. Wherever possible this data has been cross checked with other sources. In my opinion Pyramid Research have some of the best and most detailed mobile technology projections that would satisfy even the most data savvy analysts. The very extensive data analysis on Pyramid Research data sets are my own and any short falls in the analysis clearly should only be attributed to myself.
Short Message Service or SMS for short, one of the corner stones of mobile services, just turned 20 years old in 2012.
Talk about “Live Fast, Die Young” and the chances are that you are talking about SMS!
The demise of SMS has already been heralded … Mobile operators rightfully are shedding tears of the (taken-for-granted?) decline of the most profitable 140 Bytes there ever was and possible ever will be.
Before we completely kill off SMS, let’s have a brief look at
The average SMS user (across the world) consumed 136 SMS (ca. 19kByte) per month and paid 4.6 US$-cent per SMS and 2.6 US$ per month. Of course this is a worldwide average and should not be over interpreted. For example in the Philippines an average SMS user consumes 650+ SMS per month pays 0.258 US$-cent per SMS or 1.17 $ per month.The other extreme end of the SMS usage distribution we find in Cameroon with 4.6 SMS per month paying 8.19 US$-cent per SMS.
We have all seen the headlines throughout 2012 (and better part of 2011) of SMS Dying, SMS Disaster, SMS usage dropping and revenues being annihilated by OTT applications offering messaging for free, etcetcetc… & blablabla … “Mobile Operators almost clueless and definitely blameless of the SMS challenges” … Right? … hmmmm maybe not so fast!
All major market regions (i.e., WEU, CEE, NA, MEA, APAC, LA) have experienced a substantial slow down of SMS revenues in 2011 and 2012. A trend that is expected to continue and accelerate with mobile operators push for mobile broadband. Last but not least SMS volumes have slowed down as well (though less severe than the revenue slow down) as signalling-based short messaging service assimilates to IP-based messaging via mobile applications.
Irrespective of all the drama! SMS phase-out is obvious (and has been for many years) … with the introduction of LTE, SMS will be retired.
Resistance is (as the Borg’s would say) Futile!
It should be clear that the phase out of SMS does Absolutely Not mean that messaging is dead or in decline. Far far from it!
Messaging is Stronger than Ever and just got so many more communication channels beyond the signalling network of our legacy 2G & 3G networks.
Its however important to understand how long the assimilation of SMS will take and what drivers impact the speed of the SMS assimilation. From an operator strategic perspective such considerations will provide insights into how quickly they will need to replace SMS Legacy Revenues with proportional Data Revenues or suffer increasingly on both Top and Bottom line.
SMS2012 AND ITS GROWTH DYNAMICS
So lets just have a look at the numbers (with the cautionary note that some care needs to be taken with exchange rate effects between US Dollar and Local Currencies across the various markets being wrapped up in a regional and a world view. Further, due to the structure of bundling propositions, product-based revenues such as SMS Revenues, can be and often are somewhat uncertain depending on the sophistication of a given market):
2012 is expected worldwide to deliver more than 100 billion US Dollars in SMS revenues on more than 7 trillion revenue generating SMS.
The 100 Billion US Dollars is ca. 10% of total worldwide mobile turnover. This is not much different from the 3 years prior and 1+ percentage-point up compared to 2008. Data revenues excluding SMS is expected in 2012 to be beyond 350 Billion US Dollar or 3.5 times that of SMS Revenues or 30+% of total worldwide mobile turnover (5 years ago this was 20% and ca. 2+ times SMS Revenues).
SMS growth has slowed down over the last 5 years. Last 5 years SMS revenues CAGR was ca. 7% (worldwide). Between 2011 and 2012 SMS revenue growth is expected to be no more than 3%. Western Europe and Central Eastern Europe are both expected to generate less SMS revenues in 2012 than in 2011. SMS Volume grew with more than 20% per annum the last 5 years but generated SMS in 2012 is not expected to more than 10% higher than 2012.
For the ones who like to compare SMS to Data Consumption (and please safe us from ludicrous claims of the benefits of satellites and other ideas out of too many visits to Dutch Coffee shops)
2012 SMS Volume corresponds to 2.7 Terra Byte of daily data (not a lot! Really it is not!)
Don’t be terrible exited about this number! It is like Nano-Dust compared to the total mobile data volume generated worldwide.
The monthly Byte equivalent of SMS consumption is no more than 20 kilo Byte per individual mobile user in Western Europe.
Let us have a look at how this distributes across the world broken down in Western Europe (WEU), Central Eastern Europe (CEE), North America (NA), Asia Pacific (APAC), Latin America (LA) and Middle East & Africa (MEA):
From the above chart we see that
Western Europe takes almost 30% of total worldwide SMS revenues but its share of total SMS generated is less than 10%.
And to some extend also explains why Western Europe might be more exposed to SMS phase out than some other markets. We have already seen the evidence of Western Europe sensitivity to SMS revenues back in 2011, a trend that will spread in many more markets in 2012 and lead to an overall negative SMS revenue story of Western Europe in 2012. We will see that within some of the other regions there are countries that substantially more exposed to SMS phase-out than others in terms of SMS share of total mobile turnover.
In Western Europe a consumer would for an SMS pay more than 7 times the price compared to a consumer in North America (i.e., Canada or USA). It is quiet clear that Western Europe has been very successful in charging for SMS compared to any other market in the World. An consumers have gladly paid the price (well I assume so;-).
SMS Revenues in Western Europe are proportionally much more important in Western Europe than in other regions (maybe with the exception of Latin America).
In 2012 17% of Total Western Europe Mobile Turnover is expected to come from SMS Revenues (was ca. 13% in 2008).
WHAT DRIVES SMS GROWTH?
It is interesting to ask what drives SMS behaviour across various markets and countries.
Prior to reasonable good quality 3G networks and as importantly prior to the emergence of the Smartphone the SMS usage dynamics between different markets could easily be explained by relative few drivers, such as
(1) Price decline year on year (the higher decline the faster does SMS per user grow, though rate and impact will depend on Smartphone penetration & 3G quality of coverage).
(2) Price of an SMS relative to the price of a Minute (the lower the more SMS per User, in many countries there is a clear arbitrage in sending an SMS versus making a call which on average last between 60 – 120 seconds).
(3) Prepaid to Contract ratios (higher prepaid ratios tend to result in fewer SMS, though this relationship is not per se very strong).
(4) SMS ARPU to GDP (or average income if available) (The lower the higher higher the usage tend to be).
(5) 2G penetration/adaptation and
(6) literacy ratios (particular important in emerging markets. the lower the literacy rate is the lower the amount of SMS per user tend to be).
Finer detailed models can be build with many more parameters. However, the 6 given here will provide a very decent worldview of SMS dynamics (i.e., amount and growth) across countries and cultures. So for mature markets we really talk about a time before 2009 – 2010 where Smartphone penetration started to approach or exceed 20% – 30% (beyond which the model becomes a bit more complex).
In markets where the Smartphone penetration is beyond 30% and 3G networks has reached a certain coverage quality level the models describing SMS usage and growth changes to include Smartphone Penetration and to a lesser degree 3G Uptake (not Smartphone penetration and 3G uptake are not independent parameters and as such one or the other often suffice from a modelling perspective).
Looking SMS usage and growth dynamics after 2008, I have found high quality statistical and descriptive models for SMS growth using the following parameters;
(a) SMS Price Decline.
(b) SMS price to MoU Price.
(c) Prepaid percentage.
(d) Smartphone penetration (Smartphone penetration has a negative impact on SMS growth and usage – unsurprisingly!)
(e) SMS ARPU to GDP
(f) 3G penetration/uptake (Higher the 3G penetration combined with very good coverage has a negative impact on SMS growth and usage. Less important though than Smartphone penetration).
It should be noted that each of these parameters are varying with time and there for in extracting those from a comprehensive dataset time variation should be considered in order to produce a high quality descriptive model for SMS usage and growth.
If a Market and its Mobile Operators would like to protect their SMS revenues or at least slow down the assimilation of SMS, the mobile operators clearly need to understand whether pushing Smartphones and Mobile Data can make up for the decline in SMS revenues that is bound to happen with the hard push of mobile broadband devices and services.
EXPOSURE TO LOSS OF SMS REVENUE – A MARKET BY MARKET VIEW!
As we have already seen and discussed it is not surprising that SMS is declining or stagnating. At least within its present form and business model. Mobile Broadband, the Smartphone and its many applications have created a multi-verse of alternatives to the SMS. Where in the past SMS was a clear convenience and often a much cheaper alternative to an equivalent voice call, today SMS has become in-convenient and not per se a cost-efficient alternative to Voice and certainly not when compared with IP-based messaging via a given data plan.
As Mobile operators push hard for mobile broadband and inevitably increases rapidly the Smartphone penetration, SMS will decline. In the “end-game” of LTE, SMS has been altogether phased out.
Based on 2012 expectations lets look at the risk exposure that SMS phase-out brings in a market by market out-look;
We see from the above analysis that 9 markets (out of a total 74 analyzed), with Philippines taking the pole position, are having what could be characterized as a very high exposure to SMS Decline. The UK market, with more than 30% of revenues tied up in SMS, have aggressively pushed for mobile broadband and LTE. It will be very interesting to follow how UK operators will mitigate the exposure to SMS decline as LTE is penetrating the market. We will see whether LTE (and other mobile broadband propositions) can make up for the SMS decline.
More than 40 markets have an SMS revenue dependency of more than 10% of total mobile turnover and thus do have a substantial exposure to SMS decline that needs to be mitigated by changes to the messaging business model.
Mobile operators around the world still need to crack this SMS assimilation challenge … a good starting point would be to stop blaming OTT for all the evils and instead either manage their mobile broadband push and/or start changing their SMS business model to an IP-messaging business model.
IS THERE A MARGIN EXPOSURE BEYOND LOSS OF SMS REVENUES?
There is no doubt that SMS is a high-margin service, if not the highest, for The Mobile Industry.
A small de-tour into the price for SMS and the comparison with the price of mobile data!
The Basic: an SMS is 140 Bytes and max 160 characters.
On average (worldwide) an SMS user pays (i.e., in 2012) ca. 4.615 US$-cent per short message.
A Mega-Byte of data is equivalent to 7,490 SMSs which would have a “value” of ca. 345 US Dollars.
Yes! It would be if that was the price a user would pay for mobile broadband data (particular for average consumptions of 100 Mega Bytes per month of Smartphone consumption) …
However, remember that an average user (worldwide) consumes no more than 20 kilo Byte per Month.
One Mega-Byte of SMS would supposedly last for more than 50 month or more than 4 years.
This is just to illustrate the silliness of getting into SMS value comparison with mobile data.
A Byte is not just a Byte but depends what that Byte caries!
Its quiet clear that an SMS equivalent IP-based messaging does not pose much of a challenge to a mobile broadband network being it either HSPA-based or LTE-based. To some extend IP-based messaging (as long as its equivalent to 140 Bytes) should be able to be delivered at better or similar margin as in a legacy based 2G mobile network.
Thus, in my opinion a 140 Byte message should not cost more to deliver in an LTE or HSPA based network. In fact due to better spectral efficiency and at equivalent service levels, the cost of delivering 140 Bytes in LTE or HSPA should be a lot less than in GSM (or CS-3G).
However, if the mobile operators are not able to adapt their messaging business models to recover the SMS revenues (which with the margin argument above might not be $ to $ recovery but could be less) at risk of being lost to the assimilation process of pushing mobile data … well then substantial margin decline will be experienced.
Operators in the danger zone of SMS revenue exposure, and thus with the SMS revenue share exceeding 10% of the total mobile turnover, should urgently start strategizing on how they can control the SMS assimilation process without substantial financial loss to their operations.
I have made extensive use of historical and actual data from Pyramid Research country data bases. Wherever possible this data has been cross checked with other sources. Pyramid Research have some of the best and most detailed mobile technology projections that would satisfy most data savvy analysts. The very extensive data analysis on Pyramid Research data sets are my own and any short falls in the analysis clearly should only be attributed to myself.
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.
The Thousand Times Challenge, as it has been PR’ed by Qualcomm, states that the cellular capacity required in 2020 will be at least 1,000 times that of “today”. Actually, the 1,000 times challenge is referenced to the cellular demand & supply in 2010, so doing the math
the 1,000x might “only” be a 100 times challenge between now and 2020 in the world of Qualcomm’s and alike. Not that it matters! … We still talk about the same demand, just referenced to a later (and maybe less “sexy” year).
In my previous Blogs, I have accounted for the dubious affair (and non-nonsensical discussion) of over-emphasizing cellular data growth rates (see “The Thousand Times Challenge: The answer to everything about mobile data”) as well as the much more intelligent discussion about how the Mobile Industry provides for more cellular data capacity starting with the existing mobile networks (see “The Thousand Time Challenge: How to provide cellular data capacity?”).
As it turns out Cellular Network Capacity C can be described by 3 major components; (1) available bandwidth B, (2) (effective) spectral efficiency E and (3) number of cells deployed N.
The SUPPLIED NETWORK CAPACITY in Mbps (i.e., C) is equal to the AMOUNT OF SPECTRUM, i.e., available bandwidth, in MHz (i..e, B) multiplied with the SPECTRAL EFFICIENCY PER CELL in Mbps/MHz (i.e., E) multiplied by the NUMBER OF CELLS (i.e., N). For more details on how and when to apply the Cellular Network Capacity Equation read my previous Blog on “How to provide Cellular Data Capacity?”).
SK Telekom (SK Telekom’s presentation at the 3GPP workshop on “Future Radio in 3GPP” is worth a careful study) , Mallinson (@WiseHarbor) and Qualcomm (@Qualcomm_tech, and many others as of late) have used the above capacity equation to impose a Target amount of cellular network capacity a mobile network should be able to supply by 2020: Realistic or Not, this target comes to a 1,000 times the supplied capacity level in 2010 (i.e., I assume that 2010 – 2020 sounds nicer than 2012 – 2022 … although the later would have been a lot more logical to aim for if one really would like to look at 10 years … of course that might not give 1,000 times which might ruin the marketing message?).
So we have the following 2020 Cellular Network Capacity Challenge:
Thus a cellular network in 2020 should have 3 times more spectral bandwidth B available (that’s fairly easy!), 6 times higher spectral efficiency E (so so … but not impossible, particular compared with 2010) and 56 times higher cell site density N (this one might be a “real killer challenge” in more than one way), compared to 2010!.
Personally I would not get too hanged up about whether its 3 x 6 x 56 or 6 x 3 x 56 or some other “multiplicators” resulting in a 1,000 times gain (though some combinations might be a lot more feasible than others!)
Obviously we do NOT need a lot of insights to see that the 1,000x challenge is a
Rally call for Small & then Smaller Cell Deployment!
Also we do not need to be particular visionary (or have visited a Dutch Coffee Shop) to predict that by 2020 (aka The Future) compared to today (i.e., October 2012)?
Data demand from mobile devices will be a lot higher in 2020!
Cellular Networks have to (and will!) supply a lot more data capacity in 2020!
Footnote: the observant reader will have seen that I am not making the claim that there will be hugely more data traffic on the cellular network in comparison to today. The WiFi path might (and most likely will) take a lot of the traffic growth away from the cellular network.
how economical will this journey be for the Mobile Network Operator?
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
- 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.
- 2×20 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.
- 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.
- 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.
- 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!
- 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.
- might need to be Perfected & Re-farmed to another more spectral efficient technology => new infrastructure investments & customer migration cost (incl. acquisition, retention & churn).
- new deployment with coverage & service obligations => new capital investments and associated operational cost.
- demand could result in joint ventures or mergers to acquire sufficient spectrum for growth.
- 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;
- Greenfield Macro-cellular solutions (assuming this is feasible).
- Overlay (co-locate) on existing network grid.
- Sectorization of an existing site solution (i.e., moving from 3 sectors to 3 + n on same site).
Obviously, in the “extreme” cellular-demand limit where non of the above conventional means of providing additional cellular capacity are feasible, Small Cell deployment is the only alternative (besides doing nothing and letting the customer suffer). Irrespective we still need to understand how the economics will work out, as there might be instances where the most reasonable strategy is to let your customer “suffer” best-effort services. This would in particular be the case if there is no real competitive and incremental Topline incentive by adding more capacity.
Competitive circumstances could force some spectrum-starved operators to deploy small cells irrespective of it being financially unfavorable to do so.
Lets begin with the cost structure of a macro-cellular 3G Greenfield Rooftop Site Solution. We take the relevant cost structure of a configuration that we would be most likely to encounter in a Hot Traffic Zone / Metropolitan high-population density area which also is likely to be a candidate area for Small Cell deployment. The Figure below shows the Total Cost of Ownership, broken down in Annualized Capex and Annual Opex, for a Metropolitan 3G macro-cellular rooftop solution:
Note 1: The annualized Capex has been estimated assuming 5 years for RAN Infra, Backaul & Core, and 10 years for Build. It is further assumed that the site is supported by leased-fiber backhaul. Opex is the annual operational expense for maintaining the site solution.
Note 2: Operations Opex category covers Maintenance, Field-Services, Staff cost for Ops, Planning & optimization. The RAN infra Capex category covers: electronics, aggregation, antenna, cabling, installation & commissioning, etc..
Note 3: The above illustrated cost structure reflects what one should expect from a typical European operation. North American or APAC operators will have different cost distributions. Though it is not expected to change conclusions substantially (just redo the math).
When we discuss Small Cell deployment, particular as it relates to WiFi-based small cell deployment, with Infrastructure Suppliers as well as Chip Manufacturers you will get the impression that Small Cell deployment is Almost Free of Capex and Opex; i.e., hardly any build cost, free backhaul and extremely cheap infrastructure supported by no site rental, little maintenance and ultra-low energy consumption.
Obviously if Small Cells cost almost nothing, increasing cell site density with 56 times or more becomes very interesting economics … Unfortunately such ideas are wishful thinking.
For Small Cells not to substantially pressure margins and cash, Small Cell Cost Scaling needs to be very aggressive. If we talk about a 56x increase in cell site density the incremental total cost of ownership should at least be 56 times better than to deploy a macro-cellular expansion. Though let’s not fool ourselves!
No mobile operator would densify their macro cellular network 56 times if absolute cost would proportionally increase!
No Mobile operator would upsize their cellular network in any way unless it is at least margin, cost & cash neutral.
(I have no doubt that out there some are making relative business cases for small cells comparing an equivalent macro-cellular expansion versus deploying Small Cells and coming up with great cases … This would be silly of course, not that this have ever prevented such cases to be made and presented to Boards and CxOs).
The most problematic cost areas from a scaling perspective (relative to a macro-cellular Greenfield Site) are (a) Site Rental (lamp posts, shopping malls,), (b) Backhaul Cost (if relying on Cable, xDSL or Fiber connectivity), (c) Operational Cost (complexity in numbers, safety & security) and (d) Site Build Cost (legal requirements, safety & security,..).
In most realistic cases (I have seen) we will find a 1:12 to 1:20 Total Cost of Ownership difference between a Small Cell unit cost and that of a Macro-Cellular Rooftop’s unit cost. While unit Capex can be reduced very substantially, the Operational Expense scaling is a lot harder to get down to the level required for very extensive Small Cell deployments.
For a typical metropolitan rooftop (in Western Europe) we have the annualized capital expense (Capex) of ca. 15,000 Euro and operational expenses (Opex) in the order of 30,000 Euro per annum. The site-related Opex distribution would look something like this;
- Macro-cellular Rooftop 3G Site Unit Annual Opex:
- Site lease would be ca. 10,500EUR.
- Backhaul would be ca. 9,000EUR.
- Energy would be ca. 3,000EUR.
- Operations would be ca. 7,500EUR.
- i.e., total unit Opex of 30,000EUR (for average major metropolitan area)
Assuming that all cost categories could be scaled back with a factor 56 (note: very big assumption that all cost elements can be scaled back with same factor!)
- Target Unit Annual Opex cost for a Small Cell:
- Site lease should be less than 200EUR (lamp post leases substantially higher)
- Backhaul should be less than 150EUR (doable though not for carrier grade QoS).
- Energy should be less than 50EUR (very challenging for todays electronics)
- Operations should be less than 150EUR (ca. 1 hour FTE per year … challenging).
- Annual unit Opex should be less than 550EUR (not very likely to be realizable).
Similar for the Small Cell unit Capital expense (Capex) would need to be done for less than 270EUR to be fully scalable with a macro-cellular rooftop (i.e., based on 56 times scaling).
- Target Unit Annualized Capex cost for a Small Cell:
- RAN Infra should be less than 100EUR (Simple WiFi maybe doable, Cellular challenging)
- Backhaul would be less than 50EUR (simple router/switch/microwave maybe doable).
- Build would be less than 100EUR (very challenging even to cover labor).
- Core would be less than 20EUR (doable at scale).
- Annualized Capex should be less than 270EUR (very challenging to meet this target)
- Note: annualization factor: 5 years for all including Build.
So we have a Total Cost of Ownership TARGET for a Small Cell of ca. 800EUR
Inspecting the various capital as well as operational expense categories illustrates the huge challenge to be TCO comparable to a macro-cellular urban/dense-urban 3G-site configuration.
Massive Small Cell Deployment needs to be almost without incremental cost to the Mobile Network Operator to be a reasonable scenario for the 1,000 times challenge.
Most the analysis I have seen, as well as carried out myself, on real cost structure and aggressive pricing & solution designs shows that the if the Small Cell Network can be kept between 12 to 20 Cells (or Nodes) the TCO compares favorably to (i.e., beating) an equivalent macro-cellular solution. If the Mobile Operator is also a Fixed Broadband Operator (or have favorable partnership with one) there are in general better cost scaling possible than above would assume (e.g., another AT&T advantage in their DAS / Small Cell strategy).
In realistic costing scenarios so far, Small Cell economical boundaries are given by the Figure below:
Let me emphasize that above obviously assumes that an operator have a choice between deploying a Small Cell Network and conventional Cell Split, Nodal Overlay (or co-location on existing cellular site) or Sectorization (if spectral capacity allows). In the Future and in Hot Traffic Zones this might not be the case. Leaving Small Cell Network deployment or letting the customers “suffer” poorer QoS be the only options left to the mobile network operator.
So how can we (i.e., the Mobile Operator) improve the Economics of Small Cell deployment?
Having access fixed broadband such as fiber or high-quality cable infrastructure would make the backhaul scaling a lot better. Being a mobile and fixed broadband provider does become very advantageous for Small Cell Network Economics. However, the site lease (and maintenance) scaling remains a problem as lampposts or other interesting Small Cell locations might not scale very aggressively (e.g., there are examples of lamppost leases being as expensive as regular rooftop locations). From a capital investment point of view, I have my doubts whether the price will scale downwards as favorable as they would need to be. Much of the capacity gain comes from very sophisticated antenna configurations that is difficult to see being extremely cheap:
Small Cell Equipment Suppliers would need to provide a Carrier-grade solution priced at maximum 1,000EUR all included! to have a fighting chance of making massive small cell network deployment really economical.
We could assume that most of the “Small Cells” are in fact customers existing private access points (or our customers employers access points) and simply push (almost) all cellular data traffic onto those whenever a customer is in vicinity of such. All those existing and future private access points are (at least in Western Europe) connected to at least fairly good quality fixed backhaul in the form of VDSL, Cable (DOCSIS3), and eventually Fiber. This would obviously improve the TCO of “Small Cells” tremendously … Right?
Well it would reduce the MNOs TCO (as it shift the cost burden to the operator’s customer or employers of those customers) …Well … This picture also would not really be Small Cells in the sense of proper designed and integrated cells in the Cellular sense of the word, providing the operator end-2-end control of his customers service experience. In fact taking the above scenario to the extreme we might not need Small Cells at all, in the Cellular sense, or at least dramatically less than using the standard cellular capacity formula above.
In Qualcomm (as well as many infrastructure suppliers) ultimate vision the 1,000x challenge is solved by moving towards a super-heterogeneous network that consist of everything from Cellular Small Cells, Public & Private WiFi access points as well as Femto cells thrown into the equation as well.
Such an ultimate picture might indeed make the Small Cell challenge economically feasible. However, it does very fundamentally change the current operational MNO business model and it is not clear that transition comes without cost and only benefits.
Last but not least it is pretty clear than instead of 3 – 5 MNOs all going out plastering walls and lampposts with Small Cell Nodes & Antennas sharing might be an incredible clever idea. In fact I would not be altogether surprised if we will see new independent business models providing Shared Small Cell solutions for incumbent Mobile Network Operators.
Before closing the Blog, I do find it instructive to pause and reflect on lessons from Japan’s massive WiFi deployment. It might serves as a lesson to massive Small Cell Network deployment as well and an indication that collaboration might be a lot smarter than competition when it comes to such deployment:
CELLULAR DATA CAPACITY … A THOUSAND TIMES CHALLENGE?
It should be obvious that I am somewhat skeptical about all the excitement around cellular data growth rates and whether its a 1,000x or 250x or 42x (see my blog on “The Thousand Times Challenge … The answer to everything about mobile data?”). In this I share very much Dean Bubley’s (Disruptive Wireless) critical view on the “cellular growth rate craze”. See Dean’s account in his recent Blog “Mobile data traffic growth – a thought experiment and forecast”.
This obsession with cellular data growth rates is Largely Irrelevant or only serves Hysteria and Cool Blogs, Twittter and Press Headlines (which is for nothing else occasionally entertaining).
What IS Important! is how to provide more (economical) cellular capacity, avoiding;
- Massive Congestion and loss of customer service.
- Economical devastation as operator tries to supply network resources for an un-managed cellular growth profile.
(Source: adapted from K.K. Larsen “Spectrum Limitations Migrating to LTE … a Growth Market Dilemma?“)
To me the discussion of how to Increase Network Capacity with a factor THOUSAND is an altogether more interesting discussion than what the cellular growth rate might or might not be in 2020 (or any other arbitrary chosen year).
Mallinson article “The 2020 Vision for LTE” in FierceWirelessEurope gives a good summary of this effort. Though my favorite account on how to increase network capacity focusing on small cell deployment is from Iris Barcia (@ibtwi) & Simon Chapman (@simonchapman) from Keima Wireless.
So how can we simply describe cellular network capacity?
Well … it turns out that 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).
It should be understood that the best approach is to apply the formula on a per radio access technology basis, rather than across all access technologies. Also separate the analysis in Downlink capacity (i.e., from Base Station to Customer Device) and in Uplink (from consumer Device to Base Station). If averages across many access technologies or you are considering the total bandwidth B including spectrum both for Uplink and for Downlink, the spectral efficiency B needs to be averaged accordingly. Also bear in mind that there could be some inter-dependency between the (effective) spectral efficiency and number cells deployed. Though it depends on what approach you choose to take to Spectral Efficiency.
It should be remembered that not all supplied capacity is being equally utilized. Most operators have 95% of their cellular traffic confined to 50% of less of their Cells. So supplied capacity in half (or more) of most cellular operator’s network remains substantially under-utilized (i.e., 50% or more of radio network carries 5% or less of the cellular traffic … if you thought that Network Sharing would make sense … yeah it does … but its a different story;-).
Therefore I prefer to apply the cellular capacity formula to geographical limited areas of the mobile network, rather than network wide. This allows for more meaningful analysis and should avoid silly averaging effects.
So we see that providing network capacity is “pretty easy”: The more bandwidth or available spectrum we have the more cellular capacity can be provided. The better and more efficient air-interface technology the more cellular capacity and quality can we provide to our customers. Last (but not least) the more cells we have build into our mobile network the more capacity can be provided (though economics does limit this one).
The Cellular Network Capacity formula allow us to breakdown the important factors to solve the “1,000x Challenge”, which we should remember is based on a year 2010 reference (i.e., feels a little bit like cheating! right?;-) …
The Cellular Capacity Gain formula:
Basically the Cellular Network Capacity Gain in 2020 (over 2010) or the Capacity we can supply in 2020 is related to how much spectrum we have available (compared to today or 2010), the effective spectral efficiency relative improvement over today (or 2010) and the number of cells deployed in 2020 relative to today (or 2010).
According with Mallinson’s article the “1,000x Challenge” looks the following (courtesy of SK Telekom);
According with Mallinson (and SK Telekom, see “Efficient Spectrum Resource Usage for Next Generation NW” by H. Park, presented at 3GPP Workshop “on Rel.-12 and onwards”, Ljubljana, Slovenia, 11-12 June 2012) one should expect to have 3 times more spectrum available in 2020 (compared to 2010 for Cellular Data), 6 times more efficient access technology (compared to what was available in 2010) and 56 times higher cell density compared to 2010. Another important thing to remember when digesting the 3 x 6 x 56 is: this is an estimate from South Korea and SK Telekom and to a large extend driven by South Korean conditions.
Above I have emphasized the 2010 reference. It is important to remember this reference to better appreciate where the high ratios come from in the above. For example in 2010 most mobile operators where using 1 to maximum 2 carriers or in the process to upgrade to 2 carriers to credible support HSPA+. Further many operators had not transitioned to HSPA+ and few not even added HSUPA to their access layer. Furthermore, most Western European operators had on average 2 carriers for UMTS (i.e., 2×10 MHz @ 2100MHz). Some operators with a little excess 900MHz may have deployed a single carrier and either postponed 2100MHz or only very lightly deployed the higher frequency UMTS carrier in their top cities. In 2010, the 3G population coverage (defined as having minimum HSDPA) was in Western Europe at maximum 80% and in Central Eastern & Southern Europe most places maximum 60%. 3G geographical coverage always on average across the European Union was in 2010 less than 60% (in Western Europe up-to 80% and in CEE up-to 50%).
Take an European Operator with 4,000 site locations in 2010.
In 2010 this operator had deployed 3 carriers supporting HSPA @ 2100MHz (i..e, total bandwidth of 2x15MHz)
Further in 2010 the Operator also had:
- 2×10 MHz GSM @ 900MHz (with possible migration path to UMTS900).
- 2×30 MHz GSM @ 1800MHz (with possible migration path to LTE1800).
By 2020 it retained all its spectrum and gained
- 2×10 MHz @ 800MHz for LTE.
- 2×20 MHz @ 2.6GHz for LTE.
For simplicity (and idealistic reasons) let’s assume that by 2020 2G has finally been retired. Moreover, lets concern ourselves with cellular data at 3G and above service levels (i.e., ignoring GPRS & EDGE). Thus I do not distinguish between whether the air-interface is HSPA+ or LTE/LTE advanced.
OPERATOR EXAMPLE: BANDWIDTH GAIN 2010 – 2020:
The Bandwidth Gain part of the “Cellular Capacity Gain” formula is in general specific to individual operators and the particular future regulatory environment (i.e., in terms of new spectrum being released for cellular use). One should not expect a universally applicable ratio here. It will vary with a given operator’s spectrum position … Past, Present & Future.
In 2010 our Operator had 15MHz (for either DL or UL) supporting cellular data.
In 2020 the Operator should have 85MHz (for either DL or UL), which is a almost a factor 6 more than in 2010. Don’t be concerned about this not being 3! After all why should it be? Every country and operator will face different constraints and opportunities and therefor there is no reason why 3 x 6 x 56 would be a universal truth!
If Regulator’s and Lawmakers would be more friendly towards spectrum sharing the boost of available spectrum for cellular data could be a lot more.
SPECTRAL EFFICIENCY GAIN 2010 – 2020:
The Spectral Efficiency Gain part of the “Cellular Capacity Gain” formula is more universally applicable to cellular operators at the same technology stage and with a similar customer mix. Thus in general for apples and apple comparison more or less same gains should be expected.
In my experience Spectral Efficiency almost always gets experts emotions running high. More often than not there is a divide between those experts (across Operators, Suppliers, etc.) towards what would be an appropriate spectral efficiency to use in capacity assessments. Clearly everybody understands that the theoretical peak spectral efficiency is not reflecting the real service experience of customers or the amount of capacity an operator has in his Mobile Network. Thus, in general an effective (or average) spectral efficiency is being applied often based on real network measurements or estimates based on such.
When LTE was initially specified its performance targets was referenced to HSxPA Release 6. The LTE aim was to get 3 -4 times the DL spectral efficiency and 2 – 3 times the UL spectral efficiency. LTE advanced targets to double the peak spectral efficiency for both DL and UL.
At maximum expect the spectral efficiency to be:
- @Downlink to be 6 – 8 times that of Release 6.
- @Uplink to be 4 – 6 times that of Release 6.
Note that this comparison is assuming an operator’s LTE deployment would move 4×4 MiMo to 8×8 MiMo in Downlink and from 64QAM SiSo to 4×4 MiMo in Uplink. Thus a quantum leap in antenna technology and substantial antenna upgrades over the period from LTE to LTE-advanced would be on the to-do list of the mobile operators.
In theory for LTE-advanced (and depending on the 2010 starting point) one could expect a factor 6 boost in spectral efficiency by 2020 compared to 2010, as put down in the “1,000x challenge”.
However, it is highly unlikely that all devices by 2020 would be LTE-advanced. Most markets would be have at least 40% 3G penetration, some laggard markets would still have a very substantial 2G base. While LTE would be growing rapidly the share of LTE-advanced terminals might be fairly low even at 2020.
Using a x6 spectral efficiency factor by 2020 is likely being extremely optimistic.
A more realistic assessment would be a factor 3 – 4 by 2020 considering the blend of technologies in play at that time.
The critical observer sees that we have reached a capacity gain (compared to 2010) of 6 x (3-4) or 18 to 24 times. Thus to reach 1,000x we still need between 40 and 56 times the cell density.
and that translate into a lot of additional cells!
CELL DENSITY GAIN 2010 – 2020:
The Cell Density Gain part of the “Cellular Capacity Gain” formula is in general specific to individual operators and the cellular traffic demand they might experience, i.e., there is no unique universal number to be expected here.
So to get to 1,000x the capacity of 2010 we need either magic or a 50+x increase in cell density (which some may argue would amount to magic as well) …
Obviously … this sounds like a real challenge … getting more spectrum and high spectral efficiency is piece of cake compared to a 50+ times more cell density. Clearly our Mobile Operator would go broke if it would be required to finance 50 x 4000 = 200,000 sites (or cells, i.e., 3 cells = 1 macro site ). The Opex and Capex requirements would simply NOT BE PERMISSIBLE.
50+ times site density on a macro scale is Economical & Practical Nonsense … The Cellular Network Capacity heuristics in such a limit works ONLY for localized areas of a Mobile Network!
The good news is that such macro level densification would also not be required … this is where Small Cells enter the Scene. This is where you run to experts such as Simon Chapman (@simonchapman) from Keima Wireless or similar companies specialized in providing intelligent small cell deployment. Its clear that this is better done early on in the network design rather than when the capacity pressure becomes a real problem.
Note that I am currently assuming that Economics and Deployment Complexity will not become challenging with Small Cell deployment strategy … this (as we shall see) is not necessarily a reasonable assumption in all deployment scenarios.
Traffic is not equally distributed across a mobile network as the chart below clearly shows (see also Kim K Larsen’s “Capacity Planning in Mobile Data Networks Experiencing Exponential Growh in Demand”):
20% of the 3G-cells carries 60% of the data traffic and 50% of the 3G-cells carries as much as 95% of the 3G traffic.
Good news is that I might not need to worry too much about half of my cellular network that only carries 5% of my traffic.
Bad news is that up-to 50% of my cells might actually give me a substantial headache if I don’t have sufficient spectral capacity and enough customers on the most efficient access technology. Leaving me little choice but to increase my cellular network density, i.e., build more cells to my existing cellular grid.
Further, most of the data traffic is carried within the densest macro-cellular network grid (at least if an operator starts exhausting its spectral capacity with a traditional coverage grid). In a typical European City ca. 20% of Macro Cells will have a range of 300 meter or less and 50% of the Macro Cells will have a range of 500 meter or less (see below chart on “Cell ranges in a typical European City”).
Finding suitable and permissible candidates for Macro cellular cell splits below 300 meter is rather unlikely. Between 300 and 500 meter there might still be macro cellular split optionallity and if so would make the most sense to commence on (pending on future anticipated traffic growth). Above 500 meter its usually fairly likely to find suitable macro cellular site candidates (i.e., in most European Cities).
Clearly if the cellular data traffic increase would require a densification ratio of 50+ times current macro-cellular density a macro cellular alternative might be out of the question even for cell ranges up-to 2 km.
A new cellular network paradigm is required as the classical cellular network design brakes down!
Small Cell implementation is often the only alternative a Mobile Operator has to provide more capacity in a dense urban or high-traffic urban environment.
As Mobile Operators changes their cellular design, in dense urban and urban environments, to respond to the increasing cellular data demand, what kind of economical boundaries would need to be imposed to make a factor 50x increase in cell density work out.
No Mobile Operator can afford to see its Opex and Capex pressure rise! (i.e., unless revenue follows or exceed which might not be that likely).
For a moment … remember that this site density challenge is not limited to a single mobile operator … imagining that all operators (i.e., typical 3 -5 except for India with 13+;-) in a given market needs to increase their cellular site density with a factor 50. Even if there is (in theory) lots of space on the street level for Small Cells … one could imagine the regulatory resistance (not to mention consumer resistance) if a city would see a demand for Small Cell locations increase with a factor 150 – 200.
Thus, Sharing Small Cell Locations and Supporting Infrastructure will become an important trend … which should also lead to Better Economics.
This bring us to The Economics of the “1,000x Challenge” … Stay tuned!
This is not PART 2 of “Mobile Data Growth…The Perfect Storm” … This is the story of the Thousand Times Challenge!
It is not unthinkable that some mobile operators will face very substantial problems with their cellular data networks due to rapid, uncontrollable or un-managed cellular data growth. Once cellular data demand exceeds the installed base supply of network resources, the customer experience will likely suffer and cellular data consumers will no longer get the same service level that they had prior to the onset of over-demand.
One might of course argue that consumers were (and in some instances still are) spoiled during the period when mobile operators had plenty of spectral capacity available (relative to their active customer base) with unlimited data plans and very little cellular network load . As more and more customers migrate to smartphones and 3G data services, it follows naturally that there will be increasingly less spectral resources available per customer.
The above chart (from “Capacity Planning in Mobile Data Networks Experience Exponential Growth in Demand” illustrates such a situation where customers cellular data demand eventually exceeds the network capacity … which leads to a congested situation and less network resources per customer.
A mobile operator have several options that can mitigate emergence of capacity and spectrum crunch:
- Keep expand and densify the cellular network.
- Free up legacy (i.e. “old-technology”) spectrum and deploy for technology facing demand pressure.
- Introduce policy and active demand management on per user / segment level.
- Allow customers service to degrade as provider of best-effort cellular data.
- Stimulate and design for structural off-loading (levering fixed as well as cellular networks).
DEMAND … A THOUSAND TIMES FABLE?
Let me start with saying that cellular data growth does pose a formidable challenge for many mobile operators … already today … its easy to show that even at modest growth rates cellular data demand gets pretty close or beyond cellular network resources available today and in the future. Unless we fundamentally changes the way we design, plan and build networks.
However, Today The Challenge is Not network wide … At present, its limited to particular areas of the cellular networks … though as the cellular data traffic growths, the demand challenge does spread outwards and addresses an ever higher share of the cellular network.
Lately 1,000 has become a very important number. It has become the answer to the Smartphone Challenge and exponential growth of mobile data. 1000 seems to represent both demand as well as supply. Qualcomm has made it their “mission in life” (at at least the next 8 years) to solve the magic 1000 challenge. Mallinson article “The 2020 Vision for LTE” in FierceWirelessEurope gives a slightly more balanced view on demand and target supply of cellular resources: “Virtually all commentators expect a 15 to 30-fold traffic increase over five years and several expect this growth trend to last a decade to 2020, representing a 250-1,000-fold increase.” (note: the cynic in wonders about the several, its more than 2, but is it much more than 3?)
The observant reader will see that the range between minimum and maximum to be a factor of 4 … a reasonably larger error of margin to plan for. If by 2020 the demand would be 1,000 times that of demand in 2010, our Technologies better be a lot better than that as that would be an average with a long tail.
Of course most of us know that the answer really is 42! NOT 1000!
Joke aside … And let’s get serious about this 1000 Fable!
Firstly, 1,000 is (according with Qualcomm) the expected growth of data between 2010 and 2020 … Thus if data was 42 in 2010 it would be 1000×42 by 2020. That would be a CAGR of 100% over the period or a doubling of demanded data year in year our for 10 years.
… Well not really!
Qualcomm states that data demand in 2012 would be 10x that of 2010 . Thus, it follows that data demand between 2012 and 2020 “only” would be 100x or a CAGR of 78% over that period.
So in 2021 (1 year after we had 1,000x) we would see demand of ca. 1,800x, in 2022 (2 years after we solved the 1000x challenge) we would experience a demand of more than 3,000x, and so forth …
So great to solve the 1,000x challenge by 2020 but it’s going to be like “peeing in your trouser on a cold winter day” . Yes it will be warm, for a little while. Then its going to be really cold. In other words not going to help much structurally.
Could it be that this 1,000x challenge might be somewhat flawed?
- If All Commentators and Several Experts are to be believed, the growth worldwide is almost perfectly exponential with an annual growth rate between 70% and 100%.
- Growth is “unstoppable” -> unlimited sources for growth.
Actually most projections (from several expert sources;-) that I have seen does show substantial deceleration as the main source for growth exhaust, i.e., as Early & Late Majority of customers adapt to mobile data. Even Cisco own “Global Mobile Data Traffic Forecast Update, 2011 – 2016” shows an average deceleration of growth with an average of 20% per anno between 2010 and their 2014 projections (note: it’s sort of “funny” that Cisco then decide that after 2014 growth no longer slows down but stays put at 78% … alas artistic freedom I suppose?).
CELLULAR CUSTOMER MIGRATION
The following provides projection of 2G, 3G and LTE uptake between 2010 (Actual) and 2020 (Expected). The dynamics is based on latest Pyramid Research cellular projections for WEU, US, APAC, LA & CEE between 2010 to 2017. The “Last Mile”, 2018 – 2020, is based on reasonable dynamic extrapolations based on the prior period with a stronger imposed emphasis on LTE growth. Of course Pyramid Research provides one view of the technology migration and given the uncertainty on market dynamics and pricing policies are simply one view on how the cellular telco world will develop. This said, I tend to find Pyramid Research getting reasonably close to actual developments and the trends across the various markets are not that counter-intuitive.
For the US Market LTE is expected to grow very fast and reach a penetration level beyond 60% by 2020. For the other markets LTE is expected to evolve relative sluggish with an uptake percentage of 20%+/-5% by 2020. It should be remembered that all projections are averages. Thus within a market, for a specific country or operator, the technology shares could very well differ somewhat from the above.
The growth rates for LTE customer uptake over the period; 2010/2011 – 2020, 2015 – 2020 and respective LTE share in 2020.
WEU 2010-2020: 87%, 2015 – 2020: 24%, share in 2020: 20%.
USA 2010-2020: 48%, 2015 – 2020: 19%, share in 2020: 62%.
APAC 2010-2020: 118%, 2015 – 2020: 61%, share in 2020: 30%.
CEE 2011-2020: 168%, 2015 – 2020: 37%, share in 2020: 20%.
LA 2010-2020: 144%, 2015 – 2020: 37%, share in 2020: 40%.
Yes the LTE growth rates are very impressive when compared to the initial launch year with the very initial uptake. As already pointed out in my Blog …. growth rates in referenced back to a penetration less than 2% has little practical meaning. The average LTE uptake rate across all the above markets between 2012 to 2020 is 53%+/-17% (highest being APAC and Lowest being USA).
What should be evident from the above technology uptake charts are that
- 3G remains strong even in 2020 (though likely dominated by prepaid at that time).
- 2G will remain for a longtime in both CEE & APAC, even toward 2020.
In the scenario where we have a factor 100 in growth of usage between 2012 and 2020, which is a CAGR of 78%, the growth of usage per user would to be 16% pa at an annual uptake rate of 53%. However, without knowing the starting point of the LTE data usage (which initially will be very low as there is almost not users), these growth rates are not of much use and certainly cannot be used to make up any conclusions about congestion or network dire straits.
Example based on European Growth Figures:
A cellular networks have 5 mio customers, 50% Postpaid.
Network has 4,000 cell sites (12,000 sectors) that by 2020 covers both UMTS & LTE to the same depth.
in 2020 the operator have allocated 2×20 MHz for 3G & 2×20 MHz for LTE. Remaining 2G customers are one a single shared GSM network support all GSM traffic in country with no more than 2x5MHz.
By 2020 the cellular operator have ca. 4Mio 3G users and ca. 0.9Mio LTE users (remaining 100 thousand GSM customers are the real Laggards).
The 3G uptake growth rate ‘2010 – ‘2020 was 7%, between ’10 – ’12 it was 25%. 3G usage growth would not be very strong as its a blend of Late Majority and Laggards (including a fairly large Prepaid segment that appear hardly to use Cellular data).
The LTE uptake growth rate ‘2010 – ‘2020 was 87%, between ’10 – ’12 it was 458%. The first 20% of LTE would like be consisting of Innovators and Early Adopters. Thus, usage growth of LTE should be expected to be more aggressive than for 3G.
Let’s assume that 20% of the cell sites carries 50% of the devices and for simplicity also data traffic (see for example my Slideshare presentation “Capacity Planning in Mobile Data Networks Experiencing Exponential Growth in Demand” which provides evidence for such distribution).
So we have ca. 800 3G users per sector (or ca. 40 3G users per sector per MHz). By 2020, one would likewise for LTE anticipate ca. 200 LTE users per sector (or ca. 10 LTE users per sector per MHz). Note that no assumptions of activity rate has been imposed.
Irrespective of growth rate we need to ask ourselves whether 10 LTE users per sector per MHz would pose a congested situation (in the busy hour). Assume that the effective LTE spectral efficiency across a macro cellular cell would be 5Mbps/MHz/Sector. So the 10 LTE users could on average share up-to 100Mbps (@ 20MHz DL).
For 3G, where we would have 40 3G users per sector per MHz. Similar (very simple) considerations allows to conclude that the 40 4G users would have no more than 40Mbps (under semi-ideal radio conditions and @ 20MHz DL). This could be a lot more demanding and customer affecting than the resulting LTE demand, despite LTE having substantially higher growth rate than we saw for 3G over the same period.
High growth rates does not default result in cellular network breakdown. It is the absolute traffic load (in the Busy Hour) that matters.
The growth of of cellular data usage between 2010 and 2020 is likewise going to be awesome (it would be higher than above technology uptake rates).. but also pretty meaningless.
Growth rates only matter in as much as growth brings an absolute demanded traffic level above the capability of the existing network and spectral resources (supplied traffic capacity).
Irrespective of a growth rate is high, medium or low … all can cause havoc in a cellular network … some networks will handle a 1,000x without much ado, others will tumble at 250x whatever the reference point level (which also includes the network design and planning maturity levels).
However, what is important is how to provide more (economical) cellular capacity avoiding;
- Massive Congestion and loss of customer service.
- Economical devastation as operator tries to supply network resources for an un-managed cellular growth profile.
(Source: adapted from K.K. Larsen “Spectrum Limitations Migrating to LTE … a Growth Market Dilemma?“)