Full disclosure … when I was first introduced to the concept of Network Slicing, from one of the 5G fathers that I respect immensely (Rachid, it must have been back at the end of 2014), I thought that it was one of the most useless concepts that I had heard of. I did simply not see (or get) the point of introducing this level of complexity. It did not feel right. My thoughts were that taking the slicing concept to the limit might actually not make any difference to not having it, except for a tremendous amount of orchestration and management overhead (and, of course, besides the technological fun of developing it and getting it to work).
It felt a bit (a lot, actually) as a “let’s do it because we can” thinking. With the “We can” rationale based on the maturity of cloudification and softwarization frameworks, such as cloud-native, public-cloud scale, cloud computing (e.g., edge), software-defined networks (SDN), network-function virtualization (NFV), and the-one-that-is-always-named Artificial Intelligence (AI). I believed there could be other ways to offer the same variety of service experiences without this additional (what I perceived as an unnecessary) complexity. At the time, I had reservations about its impact on network planning, operations, and network efficiency. Not at all sure, it would be a development in the right economic direction.
Since then, I have softened to the concept of Network Slicing. Not (of course) that I have much choice, as slicing is an integral part of 5G standalone (5G) implementation that will be implemented and launched over the next couple of years across our industry. Who knows, I may very likely be proven very wrong, and then I learn something.
What is a network slice? We can see a network slice as an on-user-demand logical separated network partitioning, software-defined on-top of our common physical network infrastructure (wam … what a mouthful … test me out on this one next time you see me), slicing through our network technology stack and its layers. Thinking of a virtual private network (VPN) tunnel through a transport network is a reasonably good analogy. The network slice’s logical partitioning is isolated from other traffic streams (and slices) flowing through the 5G network. Apart from the slice logical isolation, it can have many different customizations, e.g., throughput, latency, scale, Quality of Service, availability, redundancy, security, etc… The user equipment initiates the slice request from a list of pre-defined slice categories. Assuming the network is capable of supporting its requirements, the chosen slice category is then created, orchestrated, and managed through the underlying physical infrastructure that makes up the network stack. The pre-defined slice categories are designed to match what our industry believe is the most essential use-cases, e.g., (a) enhanced mobile broadband use cases (eMBB), (b) ultra-reliable low-latency communications (uRLLC) use cases, (c) massive machine-type communication (MMTC) use cases, (d) Vehicular-to-anything (V2X) use-cases, etc… While the initial (early day) applications of network slicing are expected to be fairly static and configurationally relatively simple, infrastructure suppliers (e.g., Ericsson, Huawei, Nokia, …)expect network slices to become increasingly dynamic and rich in their configuration possibilities. While slicing is typically evoked for B2B and B2B2X, there is not really a reason why consumers could not benefit from network slicing as well (e.g., gaming/VR/AR, consumer smart homes, consumer vehicular applications, etc..).
Show me the money!
Ericsson and Arthur D. Little (ADL) have recently investigated the network slicing opportunities for communications service providers (CSP). Ericsson and ADL have analyzed more than 70 external market reports on the global digitalization of industries and critically reviewed more than 400 5G / digital use cases (see references in Further Readings below). They conclude that the demand from digitalization cannot be served by CSPs without Network Slicing, e.g., “Current network resources cannot match the increasing diversity of demands over time” and “Use cases will not function” (in a conventional mobile network). Thus, according to Ericsson and ADL, the industry can not “live” without Network Slicing (I guess it is good that it comes with 5G SA then). In fact, from their study, they conclude that 30% of the 5G use cases explored would require network slicing (oh joy and good luck that it will be in our networks soon).
Ericsson and ADL find globally a network slicing business potential of 200 Billion US dollars by 2030 for CSPs. With a robust CAGR (i.e., the potential will keep growing) between 23% to 36% by 2030 (i.e., CAGR estimate for period 2025 to 2030). They find that 6 Industries segments take 90+% of the slicing potential; (1) Healthcare (23%), (2) Government (17%), (3) Transportation (15%), (4) Energy & Utilities (14%), (5) Manufacturing (12%) and (6) Media & Entertainment (11%). For the keen observer, we see that the verticals are making up for most of the slicing opportunities, with only a relatively small part being related to the consumers. It should, of course, be noted that not all CSPs are necessarily also mobile network operators (MNOs), and there are also outside the strict domain of MNOs revenue potential for non-MNO CSPs (I assume).
Let us compare this slicing opportunity to global mobile industry revenue projections from 2020 to 2030. GSMA has issued a forecast for mobile revenues until 2025, expecting a total turnover of 1,140 Billion US$ in 2025 at a CAGR (2020 – 2025) of 1.26%. Assuming this compounded annual growth rate would continue to apply, we would expect a global mobile industry revenue of 1,213 Bn US$ by 2030. Our 5G deployments will contribute in the order of 621 Bn US$ (or 51% of the total). The incremental total mobile revenue between 2020 and 2030 would be ca. 140 Bn US$ (i.e., 13% over period). If we say that roughly 20% is attributed to mobile B2B business globally, we have that by 2030 we would expect a B2B turnover of 240+ Bn US$ (an increase of ca. 30 Bn US$ over 2020). So, Ericsson & ADL’s 200 Bn US$ network slicing potential is then ca. 16% of the total 2030 global mobile industry turnover or 30+% of the 5G 2030 turnover. Of course, this assumes that somehow the slicing business potential is simply embedded in the existing mobile turnover or attributed to non-MNO CSPs (monetizing the capabilities of the MNO 5G SA slicing enablers).
Of course, the Ericsson-ADL potential could also be an actual new revenue stream untapped by today’s network infrastructures due to the lack of slicing capabilities that 5G SA will bring in the following years. If so, we can look forward to a boost of the total turnover of 16% over the GSMA-based 2030 projection. Given ca. 90% of the slicing potential is related to B2B business, it may imply that B2B mobile business would almost double due to network slicing opportunities (hmmm).
Another recent study assessed that the global 5G network slicing market will reach approximately 18 Bn US$ by 2030 with a CAGR of ca. 41% over 2020-2030.
Irrespective of the slicing turnover quantum, it is unlikely that the new capabilities of 5G SA (including network slicing and much richer granular quality of service framework) will lead to new business opportunities and enable unexplored use cases. That, in turn, may indeed lead to enhanced monetization opportunities and new revenue streams between now (2022) and 2030 for our industry.
Most Western European markets will see 5G SA being launched over the next 2 to 3 years; as 5G penetration rapidly approaches 50% penetration, I expect network slicing use cases being to be tried out with CSP/MNOs, industry partners, and governmental institutions soon after 5G SA has been launched. It should be pointed out that already for some years, slicing concepts have been trialed out in various settings. Both in 4G as well as 5G NSA networks.
Prologue to Network Slicing.
5G comes with a lot of fundamental capabilities as shown in the picture below,
5G allows for (1) enhanced mobile broadband, (2) very low latency, (3) massive increase in device density handling, i.e., massive device scale-up, (4) ultra-higher network reliability and service availability, and (5) enhanced security (not shown in the above diagram) compared to previous Gs.
The service (and thus network) requirement combinations are very high. The illustration below shows two examples of mapped-out sub-set of service (and therefore also eventually slice) requirements mapped onto the major 5G capabilities. In addition, it is quite likely that businesses would have additional requirements related to slicing performance monitoring, for example, in real-time across the network stack.
and with all the various industrial or vertical use cases (see below) one could imagine (noting that there may be many many more outside our imagination), the “fathers” of 5G became (very) concerned with how such business-critical services could be orchestrated and managed within a traditional mobile network architecture as well as across various public land mobile networks (PLMN). Much of this also comes out of the wish that 5G should “conquer” (take a slice of) next-generation industries (i.e., Industry 4.0), providing additional value above and beyond “the dumb bit pipe.” Moreover, I do believe that in parallel with the wish of becoming much more relevant to Industry 4.0 (and the next generation of verticals requirements), what also played a role in the conception of network slicing is the deeply rooted engineering concept of “control being better than trust” and that “centralized control is better than decentralized” (I lost count on this debate of centralized control vs. distributed management a long time ago).
So, yes … The 5G world is about to get a lot more complex in terms of Industrial use cases that 5G should support. And yes, our consumers will expect much higher download speeds, real-time (whatever that will mean) gaming capabilities, and “autonomous” driving …
“… it’s clear that the one shared public network cannot meet the needs of emerging and advanced mobile connectivity use cases, which have a diverse array of technical operations and security requirements.” (quote from Ericsson and Arthur D. Little study, 2021).
“The diversity of requirements will only grow more disparate between use cases — the one-size-fits-all approach to wireless connectivity will no longer suffice.” (quote from Ericsson and Arthur D. Little study, 2021).
Being a naturalist (yes, I like “naked” networks), it does seem somewhat odd (to me) to say that next generation (e.g., 5G) networks cannot support all the industrious use cases that we may throw at it in its native form. Particular after having invested billions in such networks. By partitioning a network up in limiting (logically isolated), slice instances can all be supported (allegedly). I am still in the thinking phase on that one (but I don’t think the math adds up).
Now, whether one agrees (entirely) with the economic sentiment expressed by Ericsson and ADL or not. We need a richer granular way of orchestrating and managing all those diverse use-cases we expect our 5G network to support.
So, we have (or will get) network slicing with our 5G SA Core deployment. As a reminder, when we talk about a network slice, we mean;
“An on-user-demandlogicalseparated network partitioning, software-defined, on-top of a common physical network infrastructure.”
So, the customer requested the network slice, typically via a predefined menu of slicing categories that may also have been pre-validated by the relevant network. Requested slices can also be Customized,by the requester, within the underlying 5G infrastructure capabilities and functionalities. If the network can provide the requested slicing requirements, the slice is (in theory) granted. The core network then orchestrates a logically separated network partitioning throughout the relevant infrastructure resources to comply with the requested requirements (e.g., speed, latency, device scale, coverage, security, etc…). The requested partitioning (i.e., the slice) is isolated from other slices to enable (at least on a logical level) independence of other live slices. Slice Isolation is an essential concept to network slicing. Slice Elasticity ensures that resources can be scaled up and down to ensure individual slice efficiency and an overall efficient operation of all operating slices. It is possible to have a single individual network slice or partition a slice into sub-slices with their individual requirements (that does not breach the overarching slice requirements). GSMA has issued roaming and inter-PLMN guidelines to ensure 5G network slicing inter-operability when a customer’s application finds itself outside its home -PLMN.
Today, and thanks to GSMA and ITU, there are some standard network slice services pre-defined, such as (a) eMBB – Enhanced Mobile Broadband, (b) mMTC – Massive machine-type communications, (c) URLLC – Ultra-reliable low-latency communications, (d) V2X – Vehicular-to-anything communications. These identified standard network slices are called Slice Service Types (SST). SSTs are not only limited to above mentioned 4 pre-defined slice service types. The SSTs are matched to what is called a Generic Slice Template (GST) that currently, we have 37 slicing attributes, allowing for quite a big span of combinations of requirements to be specified and validated against network capabilities and functionalities (maybe there is room for some AI/ML guidance here).
The user-requested network slice that has been set up end-2-end across the network stack, between the 5G Core and the user equipment, is called the network slice instance. The whole slice setup procedure is very well described in Chapter 12 of “5G NR and enhancements, from R15 to R16. The below illustration provides a high-level illustration of various network slices,
The 5G control function Access and Mobility management Function (AMF) is the focal point for the network slicing instances. This particular architectural choice does allow for other slicing control possibilities with a higher or lower degree of core network functionality sharing between slice instances. Again the technical details are explained well in some of the reading resources provided below. The takeaway from the above illustration is that the slice instance specifications are defined for each layer and respective physical infrastructure (e.g., routers, switches, gateways, transport device in general, etc…) of the network stack (e.g., Telco Core Cloud, Backbone, Edge Cloud, Fronthaul, New Radio, and its respective air-interface). Each telco stack layer that is part of a given network slice instance is supposed to adhere strictly to the slice requirements, enabling an End-2-End, from Core to New Radio through to the user equipment, slice of a given quality (e.g., speed, latency, jitter, security, availability, etc..).
And it may be good to keep in mind that although complex industrial use cases get a lot of attention, voice and mobile broadband could easily be set up with their own slice instances and respective quality-of-services.
Network slicing examples.
All the technical network slicing “stuff” is pretty much-taken care of by standardization and provided by the 5G infrastructure solution providers (e.g., Mavenir, Huawei, Ericsson, Nokia, etc..). Figuring the technical details of how these works require an engineering or technical background and a lot of reading.
As I see it, the challenge will be in figuring out, given a use-case, the slicing requirements and whether a single slice instance suffice or multiple are required to provide the appropriate operations and fulfillment. This, I expect, will be a challenge for both the mobile network operator as well as the business partner with the use case. This assumes that the economics will come out right for more complex (e.g., dynamic) and granular slice-instance use cases. For the operator as well as for businesses and public institutions.
The illustration below provides examples of a few (out of the 37) slicing attributes for different use cases, (a) Factories with time-critical, non-time-critical, and connected goods sub-use cases (e.g., sub-slice instances, QoS differentiated), (b) Automotive with autonomous, assisted and shared view sub-use cases, (c) Health use cases, and (d) Energy use cases.
One case that I have been studying is Networked Robotics use cases for the industrial segment. Think here about ad-hoc robotic swarms (for agricultural or security use cases) or industrial production or logistics sorting lines; below are some reflections around that.
With the emergence of the 5G Core, we will also get the possibility to apply Network slicing to many diverse use cases. That there are interesting business opportunities with network slicing, I think, is clear. Whether it will add 16% to the global mobile topline by 2030, I don’t know and maybe also somewhat skeptical about (but hey, if it does … fantastic).
Today, the type of business opportunities that network slicing brings in the vertical segments is not a very big part of a mobile operator’s core competence. Mobile operators with 5G network slicing capabilities ultimately will need to build up such competence or (and!) team up with companies that have it.
That is, if the future use cases of network slicing, as envisioned by many suppliers, ultimately will get off the ground economically as well as operationally. I remain concerned that network slicing will not make operators’ operations less complex and thus will add cost (and possible failures) to their balance sheets. The “funny” thing (IMO) is that when our 5G networks are relatively unloaded, we would not have a problem delivering the use cases (obviously). Once our 5G networks are loaded, network slicing may not be the right remedy to manage traffic pressure situations or would make the quality we are providing to consumers progressively worse (and I am not sure that business and value-wise, this is a great thing to do). Of course, 6G may solve all those concerns 😉
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Also, many of my Deutsche Telekom AG and Industry colleagues, in general, have in countless ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Jia Shen, Zhongda Du, & Zhi Zhang, “5G NR and enhancements, from R15 to R16”, Elsevier Science, (2021). Provides a really good overview of what to expect from 5G standalone. Chapter 12 provides a good explanation of (and in detail account for) how 5G Network Slicing works in detail. Definitely one of my favorite books on 5G, it is not “just” an ANRA.
GSMA, “Securing the 5G Era” (2021). A good overview of security principles in 5G and how previous vulnerabilities in previous cellular generations are being addressed in 5G. This includes some explanation on why slicing further enhances security.
By the end of 2020, according with Ericsson, it was estimated that there where ca. 7.6 million 5G subscriptions in Western Europe (~ 1%). Compare this to North America’s ca. 14 million (~4%) and 190 million (~11%) North East Asia (e.g, China, South Korea, Japan, …).
Maybe Western Europe is not doing that great, when it comes to 5G penetration, in comparison with other big regional markets around the world. To some extend the reason may be that 4G network’s across most of Western Europe are performing very well and to an extend more than servicing consumers demand. For example, in The Netherlands, consumers on T-Mobile’s 4G gets, on average, a download speed of 100+ Mbps. About 5× the speed you on average would get in USA with 4G.
From the October 2021 statistics of the Global mobile Suppliers Association (GSA), 180 operators worldwide (across 72 countries) have already launched 5G. With 37% of those operators actively marketing 5G-based Fixed Wireless Access (FWA) to consumers and businesses. There are two main 5G deployment flavors; (a) non-standalone (NSA) deployment piggybacking on top of 4G. This is currently the most common deployment model, and (b) as standalone (SA) deployment, independently from legacy 4G. The 5G SA deployment model is to be expected to become the most common over the next couple of years. As of October 2021, 15 operators have launched 5G SA. It should be noted that, operators with 5G SA launched are also likely to support 5G in NSA mode as well, to provide 5G to all customers with a 5G capable handset (e.g., at the moment only 58% of commercial 5G devices supports 5G SA). Only reason for not supporting both NSA and SA would be for a greenfield operator or that the operator don’t have any 4G network (none of that type comes to my mind tbh). Another 25 operators globally are expected to be near launching standalone 5G.
It should be evident, also from the illustration below, that mobile customers globally got or will get a lot of additional download speed with the introduction of 5G. As operators introduce 5G, in their mobile networks, they will leapfrog their available capacity, speed and quality for their customers. For Europe in 2021 you would, with 5G, get an average downlink (DL) speed of 154 ± 90 Mbps compared to 2019 4G DL speed of 26 ± 8 Mbps. Thus, with 5G, in Europe, we have gained a whooping 6× in DL speed transitioning from 4G to 5G. In Asia Pacific, the quality gain is even more impressive with a 10× in DL speed and somewhat less in North America with 4× in DL speed. In general, for 5G speeds exceeding 200 Mbps on average may imply that operators have deployed 5G in the C-band band (e.g., with the C-band covering 3.3 to 5.0 GHz).
The above DL speed benchmark (by Opensignal) gives a good teaser for what to come and to expect from 5G download speed, once a 5G network is near you. There is of course much more to 5G than downlink (and uplink) speed. Some caution should be taken in the above comparison between 4G (2019) and 5G (2021) speed measurements. There are still a fair amount of networks around the world without 5G or only started upgrading their networks to 5G. I would expect the 5G average speed to reduce a bit and the speed variance to narrow as well (i.e., performance becoming more consistent).
In a previous blog I describe what to realistically expect from 5G and criticized some of the visionary aspects of the the original 5G white paper paper published back in February 2015. Of course, the tech-world doesn’t stand still and since the original 5G visionary paper by El Hattachi and Erfanian. 5G has become a lot more tangible as operators deploy it or is near deployment. More and more operators have launched 5G on-top of their 4G networks and in the configuration we define as non-standalone (i.e., 5G NSA). Within the next couple of years, coinciding with the access to higher frequencies (>2.1 GHz) with substantial (unused or underutilized) spectrum bandwidths of 50+ MHz, 5G standalone (SA) will be launched. Already today many high-end handsets support 5G SA ensuring a leapfrog in customer experience above and beyond shear mobile broadband speeds.
The below chart illustrates what to expect from 5G SA, what we already have in the “pocket” with 5G NSA, and how that may compare to existing 4G network capabilities.
There cannot be much doubt that with the introduction of the 5G Core (5GC) enabling 5G SA, we will enrich our capability and service-enabler landscape. Whether all of this cool new-ish “stuff” we get with 5G SA will make much top-line sense for operators and convenience for consumers at large is a different story for a near-future blog (so stay tuned). Also, there should not be too much doubt that 5G NSA already provide most of what the majority of our consumers are looking for (more speed).
Overall, 5G SA brings benefits, above and beyond NSA, on (a) round-trip delay (latency) which will be substantially lower in SA, as 5G does not piggyback on the slower 4G, enabling the low latency in ultra-reliable low latency communications (uRLLC), (b) a factor of 250× improvement device density (1 Million devices per km2) that can be handled supporting massive machine type communication scenarios (mMTC), (c) supports communications services at higher vehicular speeds, (d) in theory should result in low device power consumption than 5G NSA, and (e) enables new and possible less costly ways to achieve higher network (and connection) availability (e.g., with uRLLC).
Compared to 4G, 5G SA brings with it a more flexible, scalable and richer set of quality of service enablers. A 5G user equipment (UE) can have up to 1,024 so called QoS flows versus a 4G UE that can support up to 8 QoS classes (tied into the evolved packet core bearer). The advantage of moving to 5G SA is a significant reduction of QoS driven signaling load and management processing overhead, in comparison to what is the case in a 4G network. In 4G, it has been clear that the QoS enablers did not really match the requirements of many present day applications (i.e., brutal truth maybe is that the 4G QoS was outdated before it went live). This changes with the introduction of 5G SA.
So, when is it a good idea to implement 5G Standalone for mobile operators?
There are maybe three main events that should trigger operators to prepare for and launch 5G SA;
Economical demand for what 5G SA offers.
Critical mass of 5G consumers.
Want to claim being the first to offer 5G SA.
with the 3rd point being the least serious but certainly not an unlikely factor in deploying 5G SA. Apart from potentially enriching consumers experience, there are several operational advantages of transitioning to a 5GC, such as more mature IT-like cloudification of our telecommunications networks (i.e., going telco-cloud native) leading to (if designed properly) a higher degree of automation and autonomous network operations. Further, it may also allow the braver parts of telco-land to move a larger part of its network infrastructure capabilities into the public-cloud domain operated by hyperscalers or network-cloud consortia’s (if such entities will appear). Another element of the 5G SA cloud nativification (a new word?) that is frequently not well considered, is that it will allow operators to start out (very) small and scale up as business and consumer demand increases. I would expect that particular with hyperscalers and of course the-not-so-unusual-telco-supplier-suspects (e.g., Ericsson, Nokia, Huawei, Samsung, etc…), operators could launch fairly economical minimum viable products based on a minimum set of 5G SA capabilities sufficient to provide new and cost-efficient services. This will allow early entry for business-to-business new types of QoS and (or) slice-based services based on our new 5G SA capabilities.
Western Europe mobile market expectations – 5G technology share.
By end of 2021, it is expected that Western Europe would have in the order of 36 Million 5G connections, around a 5% 5G penetration. Increasing to 80 Million (11%) by end of 2022. By 2024 to 2025, it is expected that 50% of all mobile connections would be 5G based. As of October 2021 ca. 58% of commercial available mobile devices supports already 5G SA. This SA share is anticipated to grow rapidly over the next couple of years making 5G NSA increasingly unimportant.
Approaching 50% of all connections being 5G appears a very good time to aim having 5G standalone implemented and launched for operators. Also as this may coincide with substantial efforts to re-farming existing frequency spectrum from 4G to 5G as 5G data traffic exceeds that of 4G.
For Western Europe 2021, ca. 18% of the total mobile connections are business related. This number is expected to steadily increase to about 22% by 2030. With the introduction of new 5G SA capabilities, as briefly summarized above, it is to be expected that the 5G business connection share quickly will increase to the current level and that business would be able to directly monetize uRLLC, mMTC and the underlying QoS and network slicing enablers. For consumers 5G SA will bring some additional benefits but maybe less obvious new monetization possibilities, beyond the proportion of consumers caring about latency (e.g., gamers). Though, it appears likely that the new capabilities could bring operators efficiency opportunities leading to improved margin earned on consumers (for another article).
Learn as much as possible from recent IT cloudification journeys (e.g., from monolithic to cloud, understand pros and cons with lift-and-shift strategies and the intricacies of operating cloud-native environments in public cloud domains).
Aim to have 5GC available for 5G SA launch latest by 2024.
Run 5GC minimum viable product poc’s with friendly (business) users prior to bigger launch.
As 5G is launched on C-band / 3.x GHz it may likewise be a good point in time to have 5G SA available. At least for B2B customers that may benefit from uRLLC, lower latency in general, mMTC, a much richer set of QoS, network slicing, etc…
Having a solid 4G to 5G spectrum re-farming strategy ready between now and 2024 (too late imo). This should map out 4G+NSA and SA supply dynamics as increasingly customers get 5G SA capabilities in their devices.
Western Europe mobile market expectations – traffic growth.
With the growth of 5G connections and the expectation that 5G would further boost the mobile data consumption, it is expected that by 2023 – 2024, 50% of all mobile data traffic in Western Europe would be attributed to 5G. This is particular driven by increased rollout of 3.x GHz across the Western European footprint and associated massive MiMo (mMiMo) antenna deployments with 32×32 seems to be the telco-lands choice. In blended mobile data consumption a CAGR of around 34% is expected between 2020 and 2030, with 2030 having about 26× more mobile data traffic than that of 2020. Though, I suspect that in Western Europe, aggressive fiberization of telecommunications consumer and business markets, over the same period, may ultimately slow the growth (and demand) on mobile networks.
A typical Western European operator would have between 80 – 100+ MHz of bandwidth available for 4G its downlink services. The bandwidth variation being determined by how much is required of residual 3G and 2G services and whether the operator have acquired 1500MHz SDL (supplementary downlink) spectrum. With an average 4G antenna configuration of 4×4 MiMo and effective spectral efficiency of 2.25 Mbps/MHz/sector one would expect an average 4G downlink speed of 300+ Mbps per sector (@ 90 MHz committed to 4G). For 5G SA scenario with 100 MHz of 3.x GHz and 2×10 MHz @ 700 MHz, we should expect an average downlink speed of 500+ Mbps per sector for a 32×32 massive MiMo deployment at same effective spectral efficiency as 4G. In this example, although naïve, quality of coverage is ignored. With 5G, we more than double the available throughput and capacity available to the operator. So the question is whether we remain naïve and don’t care too much about the coverage aspects of 3.x GHz, as beam-forming will save the day and all will remain cheesy for our customers (if something sounds too good to be true, it rarely is true).
In an urban environment it is anticipated that with beam-forming available in our mMiMo antenna solutions downlink coverage will be reasonably fine (i.e., on average) with 3.x GHz antennas over-layed on operators existing macro-cellular footprint with minor densification required (initially). In the situation that 3.x GHz uplink cannot reach the on-macro-site antenna, the uplink can be closed by 5G @ 700 MHz, or other lower cellular frequencies available to the operator and assigned to 5G (if in standalone mode). Some concerns have been expressed in literature that present advanced higher order antenna’s (e.g., 16×16 and above ) will on average provide a poorer average coverage quality over a macro cellular area than what consumers would be used to with lower order antennas (e.g., 4×4 or lower) and that the only practical (at least with today’s state of antennas) solution would be sectorization to make up for beam forming shortfalls. In rural and sub-urban areas advanced antennas would be more suitable although the demand would be a lot less than in a busy urban environment. Of course closing the 3.x GHz with existing rural macro-cellular footprint may be a bigger challenge than in an urban clutter. Thus, massive MiMo deployments in rural areas may be much less economical and business case friendly to deploy. As more and more operators deploy 3.x GHz higher-order mMiMo more field experience will become available. So stay tuned to this topic. Although I would reserve a lot more CapEx in my near-future budget plans for substantial more sectorization in urban clutter than what I am sure is currently in most operators plans. Maybe in rural and suburban areas the need for sectorizations would be much smaller but then densification may be needed in order to provide a decent 3.x GHz coverage in general.
Western Europe mobile market expectations – 5G RAN Capex.
That brings us to another important aspect of 5G deployment, the Radio Access Network (RAN) capital expenditures (CapEx). Using my own high-level (EU-based) forecast model based on technology deployment scenario per Western European country that in general considers 1 – 3% growth in new sites per anno until 2024, then from 2025 onwards, I assuming 2 – 5% growth due to densifications needs of 5G, driven by traffic growth and before mentioned coverage limitations of 3.x GHz. Exact timing and growth percentages depends on initial 5G commercial launch, timing of 3.x GHz deployment, traffic density (per site), and site density considering a country’s surface area.
According with Statista, Western Europe had in 2018 a cellular site base of 421 thousands. Further, Statista expected this base will grow with 2% per anno in the years after 2018. This gives an estimated number of cellular sites of 438k in 2020 that has been assumed as a starting point for 2020. The model estimates that by 2030, over the next 10 years, an additional 185k (+42%) sites will have been built in Western Europe to support 5G demand. 65% (120+k) of the site growth, over the next 10 years, will be in Germany, France, Italy, Spain and UK. All countries with relative larger geographical areas that are underserved with mobile broadband services today. Countries with incumbent mobile networks, originally based on 900 MHz GSM grids (of course densified since the good old GSM days), and thus having coarser cellular grids with higher degree of mismatching the higher 5G cellular frequencies (i.e., ≥ 2.5 GHz). In the model, I have not accounted for an increased demand of sectorizations to keep coverage quality upon higher order mMiMO deployments. This, may introduce some uncertainty in the Capex assessment. However, I anticipate that sectorization uncertainty may be covered in the accelerated site demand the last 5 years of the period.
In the illustration above, the RAN capital investment assumes all sites will eventually be fiberized by 2025. That may however be an optimistic assumption and for some countries, even in Western Europe, unrealistic and possibly highly uneconomical. New sites, in my model, are always fiberized (again possibly too optimistic). Miscellaneous (Misc.) accounts for any investments needed to support the RAN and Fiber investments (e.g., Core, Transport, Cap. Labor, etc..).
In the economical estimation price erosion has been taken into account. This erosion is a blended figure accounting for annual price reduction on equipment and increases in labor cost. I am assuming a 5-year replacement cycle with an associated 10% average price increase every 5 years (on the previous year’s eroded unit price). This accounts for higher capability equipment being deployed to support the increased traffic and service demand. The economical justification for the increase unit price being that otherwise even more new sites would be required than assumed in this model. In my RAN CapEx projection model, I am assuming rational deployment, that is demand driven deployment. Thus, operators investments are primarily demand driven, e.g., only deploying infrastructure required within a given financial recovery period (e.g., depreciation period). Thus, if an operator’s demand model indicate that it will need a given antenna configuration within the financial recovery period, it deploys that. Not a smaller configuration. Not a bigger configuration. Only the one required by demand within the financial recovery period. Of course, there may be operators with other deployment incentives than pure demand driven. Though on average I suspect this would have a neglectable effect on the scale of Western Europe (i.e., on average Western Europe Telco-land is assumed to be reasonable economically rational).
All in all, demand over the next 8 years leads to an 80+ Billion Euro RAN capital expenditure, required between 2022 and 2030. This, equivalent to a annual RAN investment level of a bit under 10 Billion Euro. The average RAN CapEx to Mobile Revenue over this period would be ca. 6.3%, which is not a shockingly high level (tbh), over a period that will see an intense rollout of 5G at increasingly higher frequencies and increasingly capable antenna configurations as demand picks up. Biggest threat to capital expenditures is poor demand models (or no demand models) and planning processes investing too much too early, ultimately resulting in buyers regret and cycled in-efficient investment levels over the next 10 years. And for the reader still awake and sharp, please do note that I have not mentioned the huge elephant in the room … The associated incremental operational expense (OpEx) that such investments will incur.
As mobile revenues are not expected to increase over the period 2022 to 2030, this leaves 5G investments main purpose to maintaining current business level dominated by consumer demand. I hope this scenario will not materialize. Given how much extra quality and service potential 5G will deliver over the next 10 years, it seems rather pessimistic to assume that our customers would not be willing to pay more for that service enhancement that 5G will brings with it. Alas, time will show.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of writing this Blog. Petr Ledl, head of DTAG’s Research & Trials, and his team’s work has been a continuous inspiration to me (thank you so much for always picking up on that phone call Petr!). Also many of my Deutsche Telekom AG, T-Mobile NL & Industry colleagues in general have in countless of ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Rachid El Hattachi & Javan Erfanian , “5G White Paper”, NGMN Alliance, (February 2015). See also “5G White Paper 2” by Nick Sampson (Orange), Javan Erfanian (Bell Canada) and Nan Hu (China Mobile).
Global Mobile Frequencies Database. (last update, 25 May 2021). I recommend very much to subscribe to this database (€595,. single user license). Provides a wealth of information on spectrum portfolios across the world.
Jia Shen, Zhongda Du, & Zhi Zhang, “5G NR and enhancements, from R15 to R16”, Elsevier Science, (2021). Provides a really good overview of what to expect from 5G standalone. Particular, very good comparison with what is provided with 4G and the differences with 5G (SA and NSA).
Ali Zaidi, Fredrik Athley, Jonas Medbo, Ulf Gustavsson, Giuseppe Durisi, & Xiaoming Chen, “5G Physical Layer Principles, Models and Technology Components”, Elsevier Science, (2018). The physical layer will always pose a performance limitation on a wireless network. Fundamentally, the amount of information that can be transferred between two locations will be limited by the availability of spectrum, the laws of electromagnetic propagation, and the principles of information theory. This book provides a good description of the 5G NR physical layer including its benefits and limitations. It provides a good foundation for modelling and simulation of 5G NR.
Thomas L. Marzetta, Erik G. Larsson, Hong Yang, Hien Quoc Ngo, “Fundamentals of Massive MIMO”, Cambridge University Press, (2016). Excellent account of the workings of advanced antenna systems such as massive MiMo.
Western Europe: Western Europe has a bit of a fluid definition (I have found), here Western Europe includes the following countries comprising a population of ca. 425 Million people (in 2021); Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland United Kingdom, Andorra, Cyprus, Faeroe Islands, Greenland, Guernsey, Jersey, Malta, Luxembourg, Monaco, Liechtenstein, San Marino, Gibraltar.
As I am preparing for my keynote speech for the Annual Dinner event of the Telecom Society Netherlands (TSOC) end of January 2020, I thought the best way was to write down some of my thoughts on the key question “Is the ‘Uber’ moment for the telecom sector coming?”. In the end it turned out to be a lot more than some of my thoughts … apologies for that. Though it might still be worth reading, as many of those considerations in this piece will be hitting a telcos near you soon (if it hasn’t already).
Knowing Uber Technologies Inc’s (Uber) business model well (and knowing at least the Danish taxi industry fairly well as my family has a 70+ years old Taxi company, Radio-Taxi Nykoebing Sjaelland Denmark, started by my granddad in 1949), it instinctively appear to be an odd question … and begs the question “why would the telecom sector want an Uber moment?” … Obviously, we would prefer not to be massively loss making (as is the Uber moment at this and past moments, e.g., several billions of US$ loss over the last couple of years) and also not the regulatory & political headaches (although we have our own). Not to mention some of the negative reputation issues around “their” customer experience (quiet different from telco topics and thank you for that). Also not forgetting that Uber has access to only a fraction of the value chain in the markets the operate … Althans of course Uber is also ‘infinitely’ lighter in terms of assets than a classical Telco … Its also a bit easier to replicate an Uber (or platform businesses in general) than an asset-heavy Telco (as it requires a “bit” less cash to get started;-). But but … of course the question is more related to the type of business model Uber represent rather than the taxi / ride hailing business model itself. Thinking of Uber makes such a question more practical and tangible …
And not to forget … The super cool technology aspects of being a platform business such as Uber … maybe Telco-land can and should learn from platform businesses? … Lets roll!
Uber main business (ca. 81%) is facilitating peer-2-peer ride sharing and ride hailing services via their mobile application and its websites. Uber tabs into the sharing economy. Making use of under-utilized private cars and their owners (producers) willingness to give up hours of their time to drive others (consumers) around in their private vehicle. Uber had 95 million active users (consumers) in 2018 and is expected to reach 110 million in 2019 (22% CAGR between 2016 & 2019). Uber has around 3+ million drivers (producers) spread out over 85+ countries and 900+ cities around the world (although 1/3 is in the USA). In the third quarter of 2019, Uber did 1.77 billion trips. That is roughly 200 trips per Uber driver per month of which the median income is 155 US$ per month (1.27 US$ per trip) before gasoline and insurances. In December 2017, the median monthly salary for Americans was $3,714.
In addition Uber also provides food delivery services (i.e., Uber Eats, ca. 11%), Uber Freight services (ca. 7%) and what they call Other Bets (ca. 1%). The first 9 month of 2019, Uber spend more than 40% of the turnover on R&D. Uber has an average revenue per trip (ARPT) of ca. 2 US$ (out of 9.5 US$ per trip based on gross bookings). Not a lot of ARPT growth the last 9 quarters. Although active users (+30% YoY), trips (+31% YoY), Gross Bookings (+32%) and Adjusted Net Revenue (+35%) all shows double digit growth.
Uber allegedly takes a 25% fee of each fare (note: if you compare gross bookings, the total revenue generated by their services, to net revenue which Uber receives the average is around 20%).
Uber’s market cap, roughly 10 years after being founded, after its IPO was 76 Bn US$ (@ May 10th, 2019) only exceeded by Facebook (104.2 Bn @ IPO) and Alibaba Group (167.6 Bn US$ @ IPO). 7 month after Uber’s market cap is ca. 51 Bn US$ (-33% down on IPO). The leading European telco Deutsche Telekom AG (25 years old, 1995) in comparison has a market capitalization around 70 Bn US$ and is very far from loss making. Deutsche Telekom is one of the world’s leading integrated telecommunications companies, with some 170+ million mobile customers, 28 million fixed-network lines, and 20 million broadband lines.
Peal the Onion
“Telcos are pipe businesses, Ubers are platform businesses”
In other words, Telco’s are adhering to a classical business model with fairly linear causal value chain (see Michael Porter’s classic from 1985). It’s the type of input/output businesses that has been around since the dawn of the industrial revolution. Such a business model can (and should) have a very high degree of end-2-end customer experience control.
Ubers (e.g., Uber, Airbnb, Booking.com, ebay, Tinder, Minecraft, …) are non-linear business models that benefit from direct and indirect network effects allowing for exponential growth dynamics. Such businesses are often piggybacking on under-utilized or un-used assets owned by individuals (e.g., homes & rooms, cars, people time, etc…). Moreover, these businesses facilitate networked connectivity between consumers and producers via a digital platform. As such, platform businesses rarely have complete end-2-end customer experience control but would focus on the quality and experience of networked connectivity. While platform business have little control over their customers (i.e., consumers and producers) experiences or overall customer journey they may have indirectly via near real-time customer satisfaction feedback (although this is after the fact).
Clearly the internet has enabled many new ways of doing business. In particular it allows for digital businesses (infrastructure lite) to create value by facilitating networked-scaled business models where demand (i.e., customers demand XYZ) and supply (i.e., businesses supplying XYZ).
Think ofAirbnb‘s internet-based platform that connects (or networks) consumers (guests), who are looking for temporary accommodation (e.g., hotel room), with producers (hosts, private or corporate) of temporary accommodations to each other. Airbnb thus allow for value creation by tying into the sharing economy of private citizens. Under-utilized private property is being monetized, benefiting hosts (producers), guests (consumers) and the platform business (by charging a transactional fee). Airbnb charges hosts a 3% fee that mainly covers the payment processing cost. Moreover, Airbnb’s typical guest fee is under 13% of the booking cost. “Airbnb is a platform business built upon software and other peoples under-utilized homes & rooms”. While Airbnb facilitated private (temporary) accommodations to consumers, today there are other online platform businesses (e.g., Booking.com, Experia.com, agoda.com, … ) that facilitates connections between hotels and consumers.
Think of Uber‘sonline ride hailing platformconnects travelers (consumer) with drivers (producers, private or corporate) as an alternative to normal cab / taxi services. Uber benefits from the under-utilization of most private cars, the private owners willingness to spend spare time and desire to monetize this under-utilization by becoming a private cab driver. Again the platform business exploring the sharing economy. Uber charges their drivers 25% of the faring fee. “Uber is a platform business built upon software and other peoples under-utilized cars and spare time”.The word platform was used 747 times in Uber’s IPO document. After Uber launched its digital online ride hailing platform, many national and regional taxi applications have likewise been launched. Facilitating an easier and more convenient way of hauling a taxi, piggybacking on the penetration of smartphones in any given market. In those models official taxi businesses and licensed taxi drivers collaborate around an classical industry digital platform facilitating and managing dispatches on consumer demand.
“A platform business relies on the sharing economy, monetizing networking (i.e., connecting) consumers and producers by taking a transaction fee on the value of involved transaction flow.”
E.g., consumer pays producer, or consumer get service for free and producer pays the platform business. It is a highly scaleble business model with exponential potential for growth assuming consumers and producers alike adapt your platform. The platform business model tends to be (physical) infrastructure and asset lite and software heavy. It typically (in start-up phase at least) relies on commercially available cloud offering (e.g., Lyft relies on AWS, Uber on AWS & Google) or if the platform business is massively scaled (e.g., Facebook), the choice may be to own data center infrastructure to have better platform control over operations. Typically you will see that successful Platform businesses at scale implements hybrid cloud model levering commercially available cloud solutions and own data centers. Platform businesses tend to be heavily automated (which is relative easy in a modern cloud environment) and rely very significantly on monetizing their data with underlying state-of-the-art real-time big data systems and of course intelligent algorithmic (i.e., machine learning based) business support systems.
A platform-business’s technology stack, residing in a cloud, will typically run on a virtual machine or within a so-called container engine. The stack really resides on the upper protocol layers and is transparent to lower level protocols (e..g, physical, link, network, transport, …). In general the platform stack can be understood to function on the 3 platform layers presented in the chart to the left; (top-platform-layer) Networked Marketplace that connects producers and consumers with each other. This layer describes how a platform business customers connect (e.g., mobile app on smartphone), (middle-platform-layer) Enabling Layer in which microservices, software tools, business logic, rules and so forth will reside, (bottom-platform-layer) the Big Data Layer or Data Layer with data-driven decision making are occurring often supported by advanced real-time machine learning applications. The remaining technology stuff (e.g., physical infrastructure, servers, storage, LAN/WAN, switching, fixed and mobile telco networking, etc..) is typically taken care of by cloud or data center providers and telco providers. Which is explains why platform businesses tends to be infrastructure or asset lite (and software heavy) compared to telco and data-center providers.
“Many classical linear businesses are increasingly copying the platform businesses digital strategies (achieving an improved operational excellence) without given up on their fundamental value-chain control. Thus allowing to continue to provide consumers a known and often improved customer experience compared to a pure platform business.”
So what about the Telco model?
Well, the Telco business model is adhering to a linear value chain and business logic. And unless you are thinking of a service telco provider or virtual telco operator, Telcos are incredible infrastructure and asset heavy with massive capital investments required to provide competitive services to their customers. Apart from the required capital intensive underlying telco technology infrastructure, the telco business model requires; (1) public licenses to operate (often auctioned, or purchased and rarely “free”), (2) requires (public) telephony numbers, (3) spectrum frequencies (i.e.,for mobile operation) and so forth …
Furthermore, overall customer experience and end-2-end customer journey is very important to Telcos (as it is to most linear businesses and most would and should subscribe to being very passionate about it). In comparison to Platform Businesses, it would not be an understatement (at this moment in time at least) to say that most Telco businesses are lagging on cloudification/softwarisation, intelligent automation (whether domain-based or End-2-End) and advanced algorithmic (i.e., machine learning enabled) decision making as it relates to overarching business decisions as well as customer-related micro-decisions. However, from an economical perspective we are not talking about more than 10% – 20% of a Telco’s asset base (or capital expenses).
Mobile telco operators tend to be fairly advanced in their approaches to customer experience management, although mainly reactive rather than pro-active (due to lower intelligent algorithmic maturity again in comparison to most platform businesses). In general, fixed telco businesses are relative immature in their approaches to customer experience management (compared to mobile operators) possibly due lack of historical competitive pressure (“why care when consumers have not other choice” mindset). Alas this too is changing as more competition in fixed telco-land emerges.
“Telcos have some technology catching up to do in comparison & where relevant with platform businesses. However, that catching up does not force them to change the fundamentals of their business model (unless it make sense of course).”
Characteristic of a Platform Business
Often relies on the sharing economy (i.e., monetizing under-utilized resources).
It’s (exponential) growth relies on successful networking of consumers & producers (i.e., piggybacking on network effects).
Software-centric: platform business is software and focus / relies on the digital domain & channels.
Mobile-centric: mobile apps for consumers & producers.
Cloud-centric: platform-solution built on Public or Hybrid cloud models.
Cloud-native maturity level (i.e., the highest cloud maturity level).
Heavily end-2-end automated across cloud-native platform, processes & decision making.
Highly sophisticated data-driven decision making.
Infrastructure / asset lite (at scale may involve own data center assets).
Business driven & optimized by state-of-art big data real-time solutions supported by a very high level of data science & engineering maturity.
Little or no end-2-end customer experience control (i.e., in the sense of complete customer journey).
Very strong focus on connection experience including payment process.
Revenue source may be in form of transactional fee imposed on the value involved in networking producers and consumers (e.g., payment transaction, cost-per-click, impressions, etc..).
In my opinion it is not a given that a platform business always have to disrupt an existing market (or classical business model). However, a successful platform business often will be transformative, resulting in classical business attempting to copy aspects of the platform business model (e..g, digitalization, automation, cloud transformation, etc..). It is too early in most platform businesses life-cycle to conclude whether, where they disrupt, it is a temporary disruption (until the disrupted have transformed) or a permanently destruction of an existing classical market model (i.e., leaving little or no time for transformation).
So with the above in mind (and I am sure for many other defining factors), it is hard to see a classical telco transforming itself into a carbon copy of a platform business and maybe more importantly why this would make a lot of sense to do in the first instance. But but … it is also clear that Telco-land should proudly copy what make sense (e.g., particular around tech and level of digitization).
Teaser thought Though if you think in terms of sharing economical principles, the freedom that an eSIM (or software-based SIM equivalents) provides with 5 or more network profiles may bring to a platform business going beyond traditional MVNOs or Service Providers … well well … you think! (hint: you may still need an agreement with the classical telco though … if you are not in the club already;-). Maybe a platform model could also tab into under-utilized consumer resources that the consumer has already paid for? or what about a transactional model on Facebook (or other social media) where the consumer actual monetizes (and controls) personal information directly with third party advertisers? (actually in this model the social media company could also share part of its existing spoil earned on their consumer product, i.e., the consumer) etc…
However, it does not mean that telcos cannot (and should not) learn from some of the most successful platform business around. There certainly is enough classical beliefs in the industry that may be ripe for a bit of disruption … so untelconizing (or as my T-Mobile US friends like to call it uncarrier) ourselves may not be such a bad idea.
“There is more to telco technologies than its core network and backend platforms.”
Having a great (=successful) e-commerce business platform with cloud-native maturity level including automation that most telcos can only dream of, and mouth watering real-time big data platforms with the smartest data scientist and data engineers in the world … does not make for an easy straightforward transformation to a national (or world for that matter) leading (or non-leading) telco business in the classical sense of owning the value chain end-to-end.
Japan’s Rakuten is one platform business that has the ambition and expressed intention to move from being traditional platform-based business (ala Amazon.com) to become a mobile operator leveraging all the benefits and know-how of their existing platform technologies. Extending those principles, such as softwarization, cloudification and cloud-native automation principles, all the way out to the edge of the mobile antenna.
Many of us in telco-land thought that starting out with a classical telco, with mobile and maybe fixed assets as well, would make for an easy inclusion of platform-like technologies (as describe above), have had to revise our thinking somewhat. Certainly time-lines have been revised a couple of times, as have the assumed pre-conditions or context for such a transformation. Even economical and operational benefits that seems compelling, at least from a Greenfield perspective, turns out to be a lot more muddy when considering the legacy spaghetti we have in telcos with years and years in bag. And for the ones who keep saying that 5G will change all that … no I really doubt that it will any time soon.
While above platform-like telco topology looks so much simpler than the incumbent one … we should not forget it is what lays underneath the surface that matters. And what matters is software. Lots of software. The danger will always be present that we are ending up replacing hardware & legacy spaghetti complexity with software spaghetti complexity. Resulting unintended consequences in terms of longer-term operational stability (e.g,, when you go beyond being a greenfield business).
“Software have made a lot in the physical world redundant but it may also have leapfrogged the underlying operational complexity to an extend that may pose an existential threat down the line.”
While many platform businesses have perfected cloud-native e-commerce stacks reaching all the way out to the end-consumers mobile apps, residing on the smartphone’s OS, they do operate on the higher level of whatever relevant telco protocol stack. Platform businesses today relies on classical telcos to provide a robust connection data pipe to their end-users at high availability and stability.
What’s coming for us in Telco-land?
“Software will eat more and more of telco-land’s hardware as well as the world.”
(side note: for the ones who want to say that artificial intelligence (AI) will be eating the software, do remember that AI is software too and imo we talk then about autosarcophagy … no further comment;-).
Telcos, of the kinds with a past, will increasingly implement software solutions replacing legacy hardware functionality. Such software will be residing in a cloud environment either in form of public and/or private cloud models. We will be replacing legacy hardware-centric telco components or boxes with a software copy, residing on a boring but highly standardized hardware platform (i.e., a common off the shelf server). Yes … I talk about software definable networks (SDN) and network functional virtualization (NFV) features and functionalities (though I suspect SDN/NFV will be renamed to something else as we have talked about this for too many years for it to keep being exciting;-). The ultimate dream (or nightmare pending on taste) is to have all telco functions defined in software and operating on a very low number of standardized servers (let’s call it the pizza-box model). This is very close to the innovative and quiet frankly disruptive ideas of for example Drivenets in Israel (definitely worth a study if you haven’t already peeked at some of their solutions). We are of course seeing quiet some progress in developing software equivalents to telco core (i.e., Telco Cloud in above picture) functionalities, e.g., evolved packet core (EPC) functions, policy and charging rules function (PCRF), …. These solutions are available from the usual supplier suspects (e.g., Cisco, Ericsson, Huawei, and Nokia) as well as from (relative) new bets, such as for example Affirmed Networks and Mavenir (side note: if you are not the usual supplier suspect and have developed cloud-based telco functionalities drop me a note … particular if such work in a public or hybrid cloud model with for example Azure or AWS).
We will have software eating its way out to the edge of our telco networks. That is assuming it proves to make economical and operational sense (and maybe even anyway;-). As computing requirements, driven by softwarization of telco-land, goes “through the roof” across all network layers, edge computing centers will be deployed (or classical 2G BSC or 3G RNC sites will be re-purposed for the “lucky” operators with a more dis-aggregated network typologies).
Telcos (should) have very strong desires for platform-like automation as we know it from platform businesses cloud-native implementations. For a telco though, the question is whether they can achieve cloud-native automation principles throughout all their network layers and thus possibly allow for end-2-end (E2E) automation principles as known in a cloud-native world (which scope wise is more limited than the full telco stack). This assumes that an E2E automation goal makes economical and operational sense compared to domain-oriented automation (with domains not per see matching one to one the traditional telco network layers). While it is tempting to get all enthusiastic & winded-up about the role of artificial intelligence (AI) in telco (or any other) automation framework, it always make sense to take an ice cold shower and read up on non-AI based automation schemes as we have them in a cloud-native cloud environment before jumping into the rabbit hole. I also think that we should be very careful architecturally to spread intelligent agents all over our telco architecture and telco stack. AI will have an important mission in pro-active customer experience solutions and anomaly detection. The devil may be in how we close the loop of an intelligent agent’s output and a input to our automation framework.
To summarize what’s coming for the Telco sector;
Increased softwarization (or virtualization) moving from traditional platform layers out towards the edge.
Increased leveraging of cloud models (e.g., private, public, hybrid) following the path of softwarization.
Strive towards cloud-native operations including the obvious benefits from (non-AI based) automation that the cloud-native framework brings.
We will see a lot of focus on developing automation principles across the telco stack to the extend such will be different from cloud-native principles (note: expect there will be some at least for non-Greenfield implementations but also in general as the telco stack is not idem ditto a traditional platform stack). This may be hampered by lack of architectural standardization alignment across our industry. There is a risk that we will push for AI-based automation without exploring fully what non-AI based schemes may bring.
Inevitable the industry will spend much more efforts on developing cognitive-based pro-active customer experience solutions as well as expanding anomaly detection across the full telco stack. This will help in dealing with design complexities although might also be hampered by mis-alignment on standardization. Not to mention that AI should never become an excuse to not simplify designs and architectures.
Plus anything clever that I have not thought about or forgot to mention 🙂
So yes … softwarization, cloudification and aggressive (non-AI based) automation, known from platform-centric businesses, will be coming (in fact has arrived to an extend) for Telcos … over time and earlier for the few new brave Telco Greenfields …
Artificial intelligence based solutions will have a mission in pro-active customer experience (e.g., cellwize, uhana, …), zero-touch predictive maintenance, self-restoration & healing, and for advanced anomaly detection solutions (e.g., see Anodot as a leading example here). All are critical requirements in the new (and obviously in the old as well) telco world is being eaten by software. Self-learning “conscious” (defined in a relative narrow technical sense) anomaly detection solutions across the telco stack is in my opinion a must to deal with today’s and the future’s highly complex software architectures and systems.
I am also speculating whether intelligent agents (e.g., microagents reacting to an events) may make the telco layers less reliant on top-down control and orchestration (… I am also getting goosebumps by that idea … so maybe this is not good … hmmm … or I am cold … but then again orchestration is for non-trusting control “freaks”). Such a reactive microagent (or microservice) could take away the typical challenges with stack orchestration (e.g., blocking, waiting, …), decentralize control across the telco stack.
And no … we will not become Ubers … although there might be Ubers that will try to become us … The future will show …
I also greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of writing this Blog. Also many of my Deutsche Telekom AG, T-Mobile NL & Industry colleagues in general have in countless of ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Mike Isaac, “Super Pumped – The Battle for Uber”, 2019, W.W. Norton & Company. A good read and what starts to look like a rule of a Silicon Valley startup behavior (the very worst and of course some of the best). Irrespective of the impression this book leaves with me, I am also deeply impressed (maybe even more after reading the book;-) what Uber’s engineers have been pulling off over the last couple of years.
Chris Anderson, “Free – The Future of a Radical Price”, (2009), Hyperion eBook. This is one of the coolest books I have read on the topic of freemium, sharing economy and platform-based business models. A real revelations and indeed a confirmation that if you get something for free, you are likely not a customer but a product. A must read to understand the work around us. In this setting it is also worth reading “What is a Free Customer Worth?” by Sunil Gupta & Carl F. Mela (HBR, 2008).
Sangeet Paul Choudary, “Platform Scale”, (2015), Platform Thinking Labs Pte. Ltd. A must read for anyone thinking of developing a platform based business. Contains very good detailed end-2-end platform design recommendations. If you are interested in knowing the most important aspects of Platform business models and don’t have time for more academic deep dive, this is most likely the best book to read.
Laure Claire Reillier & Benout Reillier, “Platform Strategy”, (2017), Routledge Taylor & Francis Group. Very systematic treatment of platform economics and all strategic aspects of a platform business. It contains a fairly comprehensive overview of academic works related to platform business models and economics (that is if you want to go deeper than for example Choudary’s excellent “Platform Scale” above).
Jean-Charles Rochet & Jean Tirole, “Platform Competition in Two-sided Markets” (2003), Journal of the European Economic Association, 1, 990. Rochet & Tirole formalizes the economics of two-sided markets. The math is fairly benign but requires a mathematical background. Beside the math their paper contains some good descriptions of platform economics.
Todd W. Schneider, “Taxi and Ridehailing Usage in New York City”, a cool site that provides historical and up-to-date taxi and ride hailing usage data for New York and Chicago. This gives very interesting insights into the competitive dynamics of Uber / Ride hailing platform businesses vs the classical taxi business. It also shows that while ride hailing businesses have disrupted the taxi business in totality, being a driver for a ride hailing platform is not that great either (and as Uber continues to operate at impressive losses maybe also not for Uber either at least in their current structure).
Uber Engineeringis in general a great resource for platform / stack architecture, system design, machine learning, big data & forecasting solutions for a business model relying on real-time transactions. While I personally find the Uber architecture or system design too complex it is nevertheless an impressive solution that Uber has developed. There are many noteworthy blog posts to be found on the Uber Engineering site. Here is a couple of foundational ones (both from 2016 so please be aware that lots may have changed since then) “The Uber Engineering Tech Stack, Part I: The Foundation” (Lucie Lozinski, 2016) and “The Uber Engineering Tech Stack, Part II: The Edge and Beyond” (Lucie Lozinski, 2016) . I also found “Uber’s Big Data Platform: 100+ Petabytes with Minute Latency” post (by Reza Shiftehfar, 2018) very interesting in describing the historical development and considerations Uber went through in their big data platform as their business grew and scale became a challenge in their designs. This is really a learning resource.
Wireless One, “Rakuten: Japan’s new #4 is going all cloud”, 2019. Having had the privilege to visit Rakuten in Japan and listen to their chief-visionary Tareq Amin (CTO) they clearly start from being a platform-centric business (i.e., Asia’s Amazon.com) with the ambition to become a new breed of telco levering their platform technologies (and platform business model thinking) all the way out to the edge of the mobile base station antenna. While I love that Tareq Amin actually has gone and taken his vision from powerpoint to reality, I also think that Rakuten benefits (particular many of the advertised economical benefits) from being more a Greenfield telco than an established telco with a long history and legacy. In this respect it is humbling that their biggest stumbling block or challenge for launching their services is site rollout (yes touchy-feel infrastructure & real estate is a b*tch!). See also “Rakuten taking limited orders for services on its delayed Japan mobile network” (October, 2019).
Justin Garrison & Chris Nova, “Cloud Native Infrastructure”, 2018, O’Reilly and Kief Morris, “Infrastructure as Code”, 2016, O’Reilly. I am usually using both these books as my reference books when it comes to cloud native topics and refreshing my knowledge (and hopefully a bit of understanding).
Murat Uenlue, “The Complete Guide to the Revolutionary Platform Business Model”, 2017. Good read. Provides a great overview of platform business models and attempts systematically categorize platform businesses (e.g., Communications Platform, Social Platform, Search Platform, Open OS Platforms, Service Platforms, Asset Sharing Platforms, Payment Platforms, etc….).
100% 5G coverage is not going to happen with 30 – 300 GHz millimeter-wave frequencies alone.
The “NGMN 5G white paper” , which I will in the subsequent parts refer to as the 5G vision paper, require the 5G coverage to be 100%.
At 100% cellular coverage it becomes somewhat academic whether we talk about population coverage or geographical (area) coverage. The best way to make sure you cover 100% of population is covering 100% of the geography. Of course if you cover 100% of the geography, you are “reasonably” ensured to cover 100% of the population.
While it is theoretically possible to cover 100% (or very near to) of population without covering 100% of the geography, it might be instructive to think why 100% geographical coverage could be a useful target in 5G;
Network-augmented driving and support for varous degrees of autonomous driving would require all roads to be covered (however small).
Internet of Things (IoT) Sensors and Actuators are likely going to be of use also in rural areas (e.g., agriculture, forestation, security, waterways, railways, traffic lights, speed-detectors, villages..) and would require a network to connect to.
Given many users personal area IoT networks (e.g., fitness & health monitors, location detection, smart-devices in general) ubiquitous becomes essential.
Internet of flying things (e.g., drones) are also likely to benefit from 100% area and aerial coverage.
However, many countries remain lacking in comprehensive geographical coverage. Here is an overview of the situation in EU28 (as of 2015);
For EU28 countries, 14% of all house holds in 2015 still had no LTE coverage. This was approx.30+ million households or equivalent to 70+ million citizens without LTE coverage. The 14% might seem benign. However, it covers a Rural neglect of 64% of households not having LTE coverage. One of the core reasons for the lack of rural (population and household) coverage is mainly an economic one. Due to the relative low number of population covered per rural site and compounded by affordability issues for the rural population, overall rural sites tend to have low or no profitability. Network sharing can however improve the rural site profitability as site-related costs are shared.
From an area coverage perspective, the 64% of rural households in EU28 not having LTE coverage is likely to amount to a sizable lack of LTE coverage area. This rural proportion of areas and households are also very likely by far the least profitable to cover for any operator possibly even with very progressive network sharing arrangements.
Fixed broadband, Fiber to the Premises (FTTP) and DOCSIS3.0, lacks further behind that of mobile LTE-based broadband. Maybe not surprisingly from an business economic perspective, in rural areas fixed broadband is largely unavailable across EU28.
The chart below illustrates the variation in lack of broadband coverage across LTE, Fiber to the Premises (FTTP) and DOCSIS3.0 (i.e., Cable) from a total country perspective (i.e., rural areas included in average).
We observe that most countries have very far to go on fixed broadband provisioning (i.e., FTTP and DOCSIS3.0) and even on LTE coverage lacks complete coverage. The rural coverage view (not shown here) would be substantially worse than the above Total view.
The 5G ambition is to cover 100% of all population and households. Due to the demographics of how rural households (and populations) are spread, it is also likely that fairly large geographical areas would need to be covered in order to come true on the 100% ambition.
It would appear that bridging this lack of broadband coverage would be best served by a cellular-based technology. Given the fairly low population density in such areas relative higher average service quality (i.e., broadband) could be delivered as long as the cell range is optimized and sufficient spectrum at a relative low carrier frequency (< 1 GHz) would be available. It should be remembered that the super-high 5G 1 – 10 Gbps performance cannot be expected in rural areas. Due to the lower carrier frequency range need to provide economic rural coverage both advanced antenna systems and very large bandwidth (e.g., such as found in the mm-frequency range) would not be available to those areas. Thus limiting the capacity and peak performance possible even with 5G.
I would suspect that irrespective of the 100% ambition, telecom providers would be challenged by the economics of cellular deployment and traffic distribution. Rural areas really sucks in profitability, even in fairly aggressive sharing scenarios. Although multi-party (more than 2) sharing might be a way to minimize the profitability burden on deep rural coverage.
The above chart shows the relationship between traffic distribution and sites. As a rule of thumb 50% of revenue is typically generated by 10% of all sites (i.e., in a normal legacy mobile network) and approx. 50% of (rural) sites share roughly 10% of the revenue. Note: in emerging markets the distribution is somewhat steeper as less comprehensive rural coverage typically exist. (Source:The ABC of Network Sharing – The Fundamentals.).
Irrespective of my relative pessimism of the wider coverage utility and economics of millimeter-wave (mm-wave) based coverage, there shall be no doubt that mm-wave coverage will be essential for smaller and smallest cell coverage where due to density of users or applications will require extreme (in comparison to today’s demand) data speeds and capacities. Millimeter-wave coverage-based architectures offer very attractive / advanced antenna solutions that further will allow for increased spectral efficiency and throughput. Also the possibility of using mm-wave point to multipoint connectivity as last mile replacement for fiber appears very attractive in rural and sub-urban clutters (and possible beyond if the cost of the electronics drop according the expeced huge increase in demand for such). This last point however is in my opinion independent of 5G as Facebook with their Terragraph development have shown (i.e., 60 GHz WiGig-based system). A great account for mm-wave wireless communications systems can be found in T.S. Rappaport et al.’s book “Millimeter Wave Wireless Communications” which not only comprises the benefits of mm-wave systems but also provides an account for the challenges. It should be noted that this topic is still a very active (and interesting) research area that is relative far away from having reached maturity.
In order to provide 100% 5G coverage for the mass market of people & things, we need to engage the traditional cellular frequency bands from 600 MHz to 3 GHz.
1 – 10 Gbps PEAK DATA RATE PER USER.
Getting a Giga bit per second speed is going to require a lot of frequency bandwidth, highly advanced antenna systems and lots of additional cells. And that is likely going to lead to a (very) costly 5G deployment. Irrespective of the anticipated reduced unit cost or relative cost per Byte or bit-per-second.
At 1 Gbps it would take approx. 16 seconds to download a 2 GB SD movie. It would take less than a minute for the HD version (i.e., at 10 Gbps it just gets better;-). Say you have a 16GB smartphone, you loose maybe up to 20+% for the OS, leaving around 13GB for things to download. With 1Gbps it would take less than 2 minutes to fill up your smartphones storage (assuming you haven’t run out of credit on your data plan or reached your data ceiling before then … of course unless you happen to be a customer of T-Mobile US in which case you can binge on = you have no problems!).
The biggest share of broadband usage comes from video streaming which takes up 60% to 80% of all volumetric traffic pending country (i.e., LTE terminal penetration dependent). Providing higher speed to your customer than is required by the applied video streaming technology and smartphone or tablet display being used, seems somewhat futile to aim for. The Table below provides an overview of streaming standards, their optimal speeds and typical viewing distance for optimal experience;
So … 1Gbps could be cool … if we deliver 32K video to our customers end device, i.e., 750 – 1600 Mbps optimal data rate. Though it is hard to see customers benefiting from this performance boost given current smartphone or tablet display sizes. The screen size really have to be ridiculously large to truly benefit from this kind of resolution. Of course Star Trek-like full emersion (i.e., holodeck) scenarios would arguably require a lot (=understatement) bandwidth and even more (=beyond understatement) computing power … though such would scenario appears unlikely to be coming out of cellular devices (even in Star Trek).
1 Gbps fixed broadband plans have started to sell across Europe. Typically on Fiber networks although also on DOCSIS3.1 (10Gbps DS/1 Gbps US) networks as well in a few places. It will only be a matter of time before we see 10 Gbps fixed broadband plans being offered to consumers. Irrespective of compelling use cases might be lacking it might at least give you the bragging rights of having the biggest.
From European Commissions “Europe’s Digital Progress Report 2016”, 22 % of European homes subscribe to fast broadband access of at least 30 Mbps. An estimated 8% of European households subscribe to broadband plans of at least 100 Mbps. It is worth noticing that this is not a problem with coverage as according with the EC’s “Digital Progress Report” around 70% of all homes are covered with at least 30 Mbps and ca. 50% are covered with speeds exceeding 100 Mbps.
The chart below illustrates the broadband speed coverage in EU28;
Even if 1Gbps fixed broadband plans are being offered, still majority of European homes are at speeds below the 100 Mbps. Possible suggesting that affordability and household economics plays a role as well as the basic perceived need for speed might not (yet?) be much beyond 30 Mbps?
Most aggregation and core transport networks are designed, planned, built and operated on a assumption of dominantly customer demand of lower than 100 Mbps packages. As 1Gbps and 10 Gbps gets commercial traction, substantial upgrades are require in aggregation, core transport and last but not least possible also on an access level (to design shorter paths). It is highly likely distances between access, aggregation and core transport elements are too long to support these much higher data rates leading to very substantial redesigns and physical work to support this push to substantial higher throughputs.
Most telecommunications companies will require very substantial investments in their existing transport networks all the way from access to aggregation through the optical core switching networks, out into the world wide web of internet to support 1Gbps to 10 Gbps. Optical switching cards needs to be substantially upgraded, legacy IP/MPLS architectures might no longer work very well (i.e., scale & complexity issue).
Most analysts today believe that incumbent fixed & mobile broadband telecommunications companies with a reasonable modernized transport network are best positioned for 5G compared to mobile-only operators or fixed-mobile incumbents with an aging transport infrastructure.
What about the state of LTE speeds across Europe? OpenSignal recurrently reports on the State of LTE, the following summarizes LTE speeds in Mbps as of June 2017 for EU28 (with the exception of a few countries not included in the OpenSignal dataset);
The OpenSignal measurements are based on more than half a million devices, almost 20 billion measurements over the period of the 3 first month of 2017.
The 5G speed ambition is by todays standards 10 to 30+ times away from present 2016/2017 household fixed broadband demand or the reality of provided LTE speeds.
In essence, I can provide very high data rates in bits per second by providing a lot of frequency bandwidth B, use the most spectrally efficient technologies maximizing η, and/or add as many cells N that my economics allow for.
The average spectral efficiency is expected to be coming out in the order of 10 Mbps/MHz/cell using advanced receiver architectures, multi-antenna, multi-cell transmission and corporation. So pretty much all the high tech goodies we have in the tool box is being put to use of squeezing out as many bits per spectral Hz available and in a sustainable matter. Under very ideal Signal to Noise Ratio conditions, massive antenna arrays of up to 64 antenna elements (i.e., an optimum) seems to indicate that 50+ Mbps/MHz/Cell might be feasible in peak.
So for a spectral efficiency of 10 Mbps/MHz/cell and a demanded 1 Gbps data rate we would need 100 MHz frequency bandwidth per cell (i.e., using the above formula). Under very ideal conditions and relative large antenna arrays this might lead to a spectral requirement of only 20 MHz at 50 Mbps/MHz/Cell. Obviously, for 10 Gbps data rate we would require 1,000 MHz frequency bandwidth (1 GHz!) per cell at an average spectral efficiency of 10 Mbps/MHz/cell.
The spectral efficiency assumed for 5G heavily depends on successful deployment of many-antenna segment arrays (e.g., Massive MiMo, beam-forming antennas, …). Such fairly complex antenna deployment scenarios work best at higher frequencies, typically above 2GHz. Also such antenna systems works better at TDD than FDD with some margin on spectral efficiency. These advanced antenna solutions works perfectly in the millimeter wave range (i.e., ca. 30 – 300 GHz) where the antenna segments are much smaller and antennas can be made fairly (very) compact (note: resonance frequency of the antenna proportional to half the wavelength with is inverse proportional to the carrier frequency and thus higher frequencies need smaller material dimension to operate).
Below 2 GHz higher-order MiMo becomes increasingly impractical and the spectral efficiency regress to the limitation of a simple single-path antenna. Substantially lower than what can be achieved at much high frequencies with for example massive-MiMo.
So for the 1Gbps to 10 Gbps data rates to work out we have the following relative simple rationale;
High data rates require a lot of frequency bandwidth (>100 MHz to several GHz per channel).
Lots of frequency bandwidth are increasingly easier to find at high and very high carrier frequencies (i.e., why millimeter wave frequency band between 30 – 300 GHz is so appealing).
High and very high carrier frequencies results in small, smaller and smallest cells with very high bits per second per unit area (i.e., the area is very small!).
High and very high carrier frequency allows me to get the most out of higher order MiMo antennas (i.e., with lots of antenna elements),
Due to fairly limited cell range, I boost my overall capacity by adding many smallest cells (i.e., at the highest frequencies).
We need to watch out for the small cell densification which tends not to scale very well economically. The scaling becomes a particular problem when we need hundreds of thousands of such small cells as it is expected in most 5G deployment scenarios (i.e., particular driven by the x1000 traffic increase). The advanced antenna systems required (including the computation resources needed) to max out on spectral efficiency are likely going to be one of the major causes of breaking the economical scaling. Although there are many other CapEx and OpEx scaling factors to be concerned about for small cell deployment at scale.
Further, for mass market 5G coverage, as opposed to hot traffic zones or indoor solutions, lower carrier frequencies are needed. These will tend to be in the usual cellular range we know from our legacy cellular communications systems today (e.g., 600 MHz – 2.1 GHz). It should not be expected that 5G spectral efficiency will gain much above what is already possible with LTE and LTE-advanced at this legacy cellular frequency range. Sheer bandwidth accumulation (multi-frequency carrier aggregation) and increased site density is for the lower frequency range a more likely 5G path. Of course mass market 5G customers will benefit from faster reaction times (i.e., lower latencies), higher availability, more advanced & higher performing services arising from the very substantial changes expected in transport networks and data centers with the introduction of 5G.
Last but not least to this story … 80% and above of all mobile broadband customers usage, data as well as voice, happens in very few cells (e.g., 3!) … representing their Home and Work.
As most of the mobile cellular traffic happen at the home and at work (i.e., thus in most cases indoor) there are many ways to support such traffic without being concerned about the limitation of cell ranges.
The giga bit per second cellular service is NOT a service for the mass market, at least not in its macro-cellular form.
≤ 1 ms IN ROUND-TRIP DELAY.
A total round-trip delay of 1 or less millisecond is very much attuned to niche service. But a niche service that nevertheless could be very costly for all to implement.
Speed of light travels ca. 300 km per millisecond (ms) in vacuum and approx. 210 km per ms in fiber (some material dependency here). Lately engineers have gotten really excited about the speed of light not being fast enough and have made a lot of heavy thinking abou edge this and that (e.g., computing, cloud, cloudlets, CDNs,, etc…). This said it is certainly true that most modern data centers have not been build taking too much into account that speed of light might become insufficient. And should there really be a great business case of sub-millisecond total (i.e., including the application layer) roundtrip time scales edge computing resources would be required a lot closer to customers than what is the case today.
It is common to use delay, round-trip time or round-trip delay, or latency as meaning the same thing. Though it is always cool to make sure people really talk about the same thing by confirming that it is indeed a round-trip rather than single path. Also to be clear it is worthwhile to check that all people around the table talk about delay at the same place in the OSI stack or network path or whatever reference point agreed to be used.
In the context of the 5G vision paper it is emphasized that specified round-trip time is based on the application layer (i.e., OSI model) as reference point. It is certainly the most meaningful measure of user experience. This is defined as the End-2-End (E2E) Latency metric and measure the complete delay traversing the OSI stack from physical layer all the way up through network layer to the top application layer, down again, between source and destination including acknowledgement of a successful data packet delivery.
The 5G system shall provide 10 ms E2E latency in general and 1 ms E2E latency for use cases requiring extremely low latency.
The 5G vision paper states “Note these latency targets assume the application layer processing time is negligible to the delay introduced by transport and switching.” (Section 4.1.3 page 26 in “NGMN 5G White paper”).
In my opinion it is a very substantial mouthful to assume that the Application Layer (actually what is above the Network Layer) will not contribute significantly to the overall latency. Certainly for many applications residing outside the operators network borders, in the world wide web, we can expect a very substantial delay (i.e., even in comparison with 10 ms). Again this aspect was also addressed in my two first chapters.
Very substantial investments are likely needed to meet E2E delays envisioned in 5G. In fact the cost of improving latencies gets prohibitively more expensive as the target is lowered. The overall cost of design for 10 ms would be a lot less costly than designing for 1 ms or lower. The network design challenge if 1 millisecond or below is required, is that it might not matter that this is only a “service” needed in very special situations, overall the network would have to be designed for the strictest denominator.
Moreover, if remedies needs to be found to mitigate likely delays above the Network Layer, distance and insufficient speed of light might be the least of worries to get this ambition nailed (even at the 10 ms target). Of course if all applications are moved inside operator’s networked premises with simpler transport paths (and yes shorter effective distances) and distributed across a hierarchical cloud (edge, frontend, backend, etc..), the assumption of negligible delay in layers above the Network Layer might become much more likely. However, it does sound a lot like America Online walled garden fast forward to the past kind of paradigm.
So with 1 ms E2E delay … yeah yeah … “play it again Sam” … relevant applications clearly need to be inside network boundary and being optimized for processing speed or silly & simple (i.e., negligible delay above the Network Layer), no queuing delay (to the extend of being in-efficiency?), near-instantaneous transmission (i.e., negligible transmission delay) and distances likely below tenth of km (i.e., very short propagation delay).
When the speed of light is too slow there are few economic options to solve that challenge.
≥ 10,000 Gbps / Km2 DATA DENSITY.
The data density is maybe not the most sensible measure around. If taken too serious could lead to hyper-ultra dense smallest network deployments.
This has always been a fun one in my opinion. It can be a meaningful design metric or completely meaningless.
There is of course nothing particular challenging in getting a very high throughput density if an area is small enough. If I have a cellular range of few tens of meters, say 20 meters, then my cell area is smaller than 1/1000 of a km2. If I have 620 MHz bandwidth aggregated between 28 GHz and 39 GHz (i.e., both in the millimeter wave band) with a 10 Mbps/MHz/Cell, I could support 6,200 Gbps/km2. That’s almost 3 Petabyte in an hour or 10 years of 24/7 binge watching of HD videos. Note given my spectral efficiency is based on an average value, it is likely that I could achieve substantially more bandwidth density and in peaks closer to the 10,000 Gbps/km2 … easily.
Pretty Awesome Wow!
The basic; a Terabit equals 1024 Gigabits (but I tend to ignore that last 24 … sorry I am not).
With a traffic density of ca. 10,000 Gbps per km2, one would expect to have between 1,000 (@ 10 Gbps peak) to 10,000 (@ 1 Gbps peak) concurrent users per square km.
At 10 Mbps/MHz/Cell one would expect to have a 1,000 Cell-GHz/km2. Assume that we would have 1 GHz bandwidth (i.e., somewhere in the 30 – 300 GHz mm-wave range), one would need 1,000 cells per km2. On average with a cell range of about 20 meters (smaller to smallest … I guess what Nokia would call an Hyper-Ultra-Dense Network;-). Thus each cell would minimum have between 1 to 10 concurrent users.
Just as a reminder! 1 minutes at 1 Gbps corresponds to 7.5 GB. A bit more than what you need for a 80 minute HD (i.e., 720pp) full movie stream … in 1 minutes. So with your (almost) personal smallest cell what about the remaining 59 minutes? Seems somewhat wasteful at least until kingdom come (alas maybe sooner than that).
It would appear that the very high 5G data density target could result in very in-efficient networks from a utilization perspective.
≥ 1 MN / Km2 DEVICE DENSITY.
One million 5G devices per square kilometer appears to be far far out in a future where one would expect us to be talking about 7G or even higher Gs.
1 Million devices seems like a lot and certainly per km2. It is 1 device per square meter on average. A 20 meter cell-range smallest cell would contain ca. 1,200 devices.
To give this number perspective lets compare it with one of my favorite South-East Asian cities. The city with one of the highest population densities around, Manila (Philippines). Manila has more than 40 thousand people per square km. Thus in Manila this would mean that we would have about 24 devices per person or 100+ per household per km2. Overall, in Manila we would then expect approx. 40 million devices spread across the city (i.e., Manila has ca. 1.8 Million inhabitants over an area of 43 km2. Philippines has a population of approx. 100 Million).
Just for the curious, it is possible to find other more populated areas in the world. However, these highly dense areas tends to be over relative smaller surface areas, often much smaller than a square kilometer and with relative few people. For example Fadiouth Island in Dakar have a surface area of 0.15 km2 and 9,000 inhabitants making it one of the most pop densest areas in the world (i.e., 60,000 pop per km2).
I hope I made my case! A million devices per km2 is a big number.
Let us look at it from a forecasting perspective. Just to see whether we are possibly getting close to this 5G ambition number.
IHS forecasts 30.5 Billion installed devices by 2020, IDC is also believes it to be around 30 Billion by 2020. Machina Research is less bullish and projects 27 Billion by 2025 (IHS expects that number to be 75.4 Billion) but this forecast is from 2013. Irrespective, we are obviously in the league of very big numbers. By the way 5G IoT if at all considered is only a tiny fraction of the overall projected IoT numbers (e.g., Machine Research expects 10 Million 5G IoT connections by 2024 …that is extremely small numbers in comparison to the overall IoT projections).
To break this number down to something that could be more meaningful than just being Big and impressive, let just establish a couple of worldish numbers that can help us with this;
2020 population expected to be around 7.8 Billion compared to 2016 7.4 Billion.
Global pop per HH is ~3.5 (average number!) which might be marginally lower in 2020. Urban populations tend to have less pop per households ca. 3.0. Urban populations in so-called developed countries are having a pop per HH of ca. 2.4.
ca. 55% of world population lives in Urban areas. This will be higher by 2020.
Less than 20% of world population lives in developed countries (based on HDI). This is a 2016 estimate and will be higher by 2020.
World surface area is 510 Million km2 (including water).
of which ca. 150 million km2 is land area
of which ca. 75 million km2 is habitable.
of which 3% is an upper limit estimate of earth surface area covered by urban development, i.e., 15.3 Million km2.
of which approx. 1.7 Million km2 comprises developed regions urban areas.
ca. 37% of all land-based area is agricultural land.
Using 30 Billion IoT devices by 2020 is equivalent to;
ca. 4 IoT per world population.
ca. 14 IoT per world households.
ca. 200 IoT per km2 of all land-based surface area.
ca. 2,000 IoT per km2 of all urban developed surface area.
If we limit IoT’s in 2020 to developed countries, which wrongly or rightly exclude China, India and larger parts of Latin America, we get the following by 2020;
ca. 20 IoT per developed country population.
ca. 50 IoT per developed country households.
ca. 18,000 IoT per km2 developed country urbanized areas.
Given that it would make sense to include larger areas and population of both China, India and Latin America, the above developed country numbers are bound to be (a lot) lower per Pop, HH and km2. If we include agricultural land the number of IoTs will go down per km2.
So far far away from a Million IoT per km2.
What about parking spaces, for sure IoT will add up when we consider parking spaces!? … Right? Well in Europe you will find that most big cities will have between 50 to 200 (public) parking spaces per square kilometer (e.g., ca. 67 per km2 for Berlin and 160 per km2 in Greater Copenhagen). Aha not really making up to the Million IoT per km2 … what about cars?
In EU28 there are approx. 256 Million passenger cars (2015 data) over a population of ca. 510 Million pops (or ca. 213 million households). So a bit more than 1 passenger car per household on EU28 average. In Eu28 approx. 75+% lives in urban area which comprises ca. 150 thousand square kilometers (i.e., 3.8% of EU28’s 4 Million km2). So one would expect little more (if not a little less) than 1,300 passenger cars per km2. You may say … aha but it is not fair … you don’t include motor vehicles that are used for work … well that is an exercise for you (too convince yourself why that doesn’t really matter too much and with my royal rounding up numbers maybe is already accounted for). Also consider that many EU28 major cities with good public transportation are having significantly less cars per household or population than the average would allude to.
Surely, public street light will make it through? Nope! Typical bigger modern developed country city will have on average approx. 85 street lights per km2, although it varies from 0 to 1,000+. Light bulbs per residential household (from a 2012 study of the US) ranges from 50 to 80+. In developed countries we have roughly 1,000 households per km2 and thus we would expect between 50 thousand to 80+ thousand lightbulbs per km2. Shops and business would add some additions to this number.
With a cumulated annual growth rate of ca. 22% it would take 20 years (from 2020) to reach a Million IoT devices per km2 if we will have 20 thousand per km2 by 2020. With a 30% CAGR it would still take 15 years (from 2020) to reach a Million IoT per km2.
The current IoT projections of 30 Billion IoT devices in operation by 2020 does not appear to be unrealistic when broken down on a household or population level in developed areas (even less ambitious on a worldwide level). The 18,000 IoT per km2 of developed urban surface area by 2020 does appear somewhat ambitious. However, if we would include agricultural land the number would become possible a more reasonable.
If you include street crossings, traffic radars, city-based video monitoring (e.g., London has approx. 300 per km2, Hong Kong ca. 200 per km2), city-based traffic sensors, environmental sensors, etc.. you are going to get to sizable numbers.
Maybe the 1 Million Devices per km2 ambition is not one of the most important 5G design criteria’s for the short term (i.e., next 10 – 20 years).
Oh and most IoT forecasts from the period 2015 – 2016 does not really include 5G IoT devices in particular. The chart below illustrates Machina Research IoT forecast for 2024 (from August 2015). In a more recent forecast from 2016, Machine Research predict that by 2024 there would be ca. 10 million 5G IoT connections or 0.04% of the total number of forecasted connections;
The winner is … IoTs using WiFi or other short range communications protocols. Obviously, the cynic in me (mea culpa) would say that a mm-wave based 5G connections can also be characterized as short range … so there might be a very interesting replacement market there for 5G IoT … maybe? 😉
Expectations to 5G-based IoT does not appear to be very impressive at least over the next 10 years and possible beyond.
The un-importance of 5G IoT should not be a great surprise given most 5G deployment scenarios are focused on millimeter-wave smallest 5G cell coverage which is not good for comprehensive coverage of IoT devices not being limited to those very special 5G coverage situations being thought about today.
Only operators focusing on comprehensive 5G coverage re-purposing lower carrier frequency bands (i.e., 1 GHz and lower) can possible expect to gain a reasonable (as opposed to niche) 5G IoT business. T-Mobile US with their 600 MHz 5G strategy might very well be uniquely positions for taking a large share of future proof IoT business across USA. Though they are also pretty uniquely position for NB-IoT with their comprehensive 700MHz LTE coverage.
For 5G IoT to be meaningful (at scale) the conventional macro-cellular networks needs to be in play for 5G coverage .,, certainly 100% 5G coverage will be a requirement. Although, even with 5G there maybe 100s of Billion of non-5G IoT devices that require coverage and management.
≤ 500 km/h SERVICE SUPPORT.
Sure why not? but why not faster than that? At hyperloop or commercial passenger airplane speeds for example?
Before we get all excited about Gbps speeds at 500 km/h, it should be clear that the 5G vision paper only proposed speeds between 10 Mbps up-to 50 Mbps (actually it is allowed to regress down to 50 kilo bits per second). With 200 Mbps for broadcast like services.
So in general, this is a pretty reasonable requirement. Maybe with the 200 Mbps for broadcasting services being somewhat head scratching unless the vehicle is one big 16K screen. Although the users proximity to such a screen does not guaranty an ideal 16K viewing experience to say the least.
What moves so fast?
The fastest train today is tracking at ca. 435 km/h (Shanghai Maglev, China).
Typical cruising airspeed for a long-distance commercial passenger aircraft is approx. 900 km/h. So we might not be able to provide the best 5G experience in commercial passenger aircrafts … unless we solve that with an in-plane communications system rather than trying to provide Gbps speed by external coverage means.
Why take a plane when you can jump on the local Hyperloop? The proposed Hyperloop should track at an average speed of around 970 km/h (faster or similar speeds as commercial passengers aircrafts), with a top speed of 1,200 km/h. So if you happen to be in between LA and San Francisco in 2020+ you might not be able to get the best 5G service possible … what a bummer! This is clearly an area where the vision did not look far enough.
Providing services to moving things at a relative fast speed does require a reasonable good coverage. Whether it being train track, hyperloop tunnel or ground to air coverage of commercial passenger aircraft, new coverage solutions would need to be deployed. Or alternative in-vehicular coverage solutions providing a perception of 5G experience might be an alternative that could turn out to be more economical.
The speed requirement is a very reasonable one particular for train coverage.
50% TOTAL NETWORK ENERGY REDUCTION.
If 5G development could come true on this ambition we talk about 10 Billion US Dollars (for the cellular industry). Equivalent to a percentage point on the margin.
There are two aspects of energy efficiency in a cellular based communication system.
User equipment that will benefit from longer intervals without charging and thus improve customers experience and overall save energy from less frequently charges.
Network infrastructure energy consumption savings will directly positively impact a telecom operators Ebitda.
Energy efficient Smartphones
The first aspect of user equipment is addressed by the 5G vision paper under “4.3 Device Requirements” sub-section “4.3.3 Device Power Efficiency”; “Battery life shall be significantly increased: at least 3 days for a smartphone, and up tp 15 years for a low-cost MTC device.”(note: MTC = Machine Type Communications).
Apple’s iPhone 7 battery life (on a full charge) is around 6 hours of constant use with 7 Plus beating that with ca. 3 hours (i.e., total 9 hours). So 3 days will go a long way.
It is however unclear whether the 3 extra days of a 5G smartphone battery life-time is supposed to be under active usage conditions or just in idle mode. Obviously in order to matter materially to the consumer one would expect this vision to apply to active usage (i.e., 4+ hours a day at 100s of Mbps – 1Gbps operations).
Energy efficient network infrastructure.
The 5G vision paper defines energy efficiency as number of bits that can be transmitted over the telecom infrastructure per Joule of Energy.
The total energy cost, i.e., operational expense (OpEx), of telecommunications network can be considerable. Despite our mobile access technologies having become more energy efficient with each generation, the total OpEx of energy attributed to the network infrastructure has increased over the last 10 years in general. The growth in telco infrastructure related energy consumption has been driven by the consumer demand for broadband services in mobile and fixed including incredible increase in data center computing and storage requirements.
In general power consumption OpEx share of total technology cost amounts to 8% to 15% (i.e., for Telcos without heavy reliance of diesel). The general assumption is that with regular modernization, energy efficiency gain in newer electronics can keep growth in energy consumption to a minimum compensating for increased broadband and computing demand.
Note: Technology Opex (including NT & IT) on average lays between 18% to 25% of total corporate Telco Opex. Out of the Technology Opex between 8% to 15% (max) can typically be attributed to telco infrastructure energy consumption. The access & aggregation contribution to the energy cost typically would towards 80% plus. Data centers are expected to increasingly contribute to the power consumption and cost as well. Deep diving into the access equipment power consumption, ca. 60% can be attributed to rectifiers and amplifiers, 15% by the DC power system & miscellaneous and another 25% by cooling.
5G vision paper is very bullish in their requirement to reduce the total energy and its associated cost; it is stated “5G should support a 1,000 times traffic increase in the next 10 years timeframe, with an energy consumption by the whole network of only half that typically consumed by today’s networks. This leads to the requirement of an energy efficiency of x2,000 in the next 10 years timeframe.” (sub-section “4.6.2 Energy Efficiency” NGMN 5G White Paper).
This requirement would mean that in a pure 5G world (i.e., all traffic on 5G), the power consumption arising from the cellular network would be 50% of what is consumed today. In 2016 terms the Mobile-based Opex saving would be in the order of 5 Billion US$ to 10+ Billion US$ annually. This would be equivalent to 0.5% to 1.1% margin improvement globally (note: using GSMA 2016 Revenue & Growth data and Pyramid Research forecast). If energy price would increase over the next 10 years the saving / benefits would of course be proportionally larger.
As we have seen in the above, it is reasonable to expect a very considerable increase in cell density as the broadband traffic demand increases from peak bandwidth (i.e., 1 – 10 Gbps) and traffic density (i.e., 1 Tbps per km2) expectations.
Depending on the demanded traffic density, spectrum and carrier frequency available for 5G between 100 to 1,000 small cell sites per km2 could be required over the next 10 years. This cell site increase will be required in addition to existing macro-cellular network infrastructure.
Today (in 2017) an operator in EU28-sized country may have between ca. 3,500 to 35,000 cell sites with approx. 50% covering rural areas. Many analysts are expecting that for medium sized countries (e.g., with 3,500 – 10,000 macro cellular sites), operators would eventually have up-to 100,000 small cells under management in addition to their existing macro-cellular sites. Most of those 5G small cells and many of the 5G macro-sites we will have over the next 10 years, are also going to have advanced massive MiMo antenna systems with many active antenna elements per installed base antenna requiring substantial computing to gain maximum performance.
It appears with today’s knowledge extremely challenging (to put it mildly) to envision a 5G network consuming 50% of today’s total energy consumption.
It is highly likely that the 5G radio node electronics in a small cell environment (and maybe also in a macro cellular environment?) will consume less Joules per delivery bit (per second) due to technology advances and less transmitted power required (i.e., its a small or smallest cell). However, this power efficiency technology and network cellular architecture gain can very easily be destroyed by the massive additional demand of small, smaller and smallest cells combined with highly sophisticated antenna systems consuming additional energy for their compute operations to make such systems work. Furthermore, we will see operators increasingly providing sophisticated data center resources network operations as well as for the customers they serve. If the speed of light is insufficient for some services or country geographies, additional edge data centers will be introduced, also leading to an increased energy consumption not present in todays telecom networks. Increased computing and storage demand will also make the absolute efficiency requirement highly challenging.
Will 5G be able to deliver bits (per second) more efficiently … Yes!
Will 5G be able to reduce the overall power consumption of todays telecom networks with 50% … highly unlikely.
In my opinion the industry will have done a pretty good technology job if we can keep the existing energy cost at the level of today (or even allowing for unit price increases over the next 10 years).
The Total power reduction of our telecommunications networks will be one of the most important 5G development tasks as the industry cannot afford a new technology that results in waste amount of incremental absolute cost. Great relative cost doesn’t matter if it results in above and beyond total cost.
≥ 99.999% NETWORK AVAILABILITY & DATA CONNECTION RELIABILITY.
A network availability of 5Ns across all individual network elements and over time correspond to less than a second a day downtime anywhere in the network. Few telecom networks are designed for that today.
5 Nines (5N) is a great aspiration for services and network infrastructures. It also tends to be fairly costly and likely to raise the level of network complexity. Although in the 5G world of heterogeneous networks … well its is already complicated.
5N Network Availability.
From a network and/or service availability perspective it means that over the cause of the day, your service should not experience more than 0.86 seconds of downtime. Across a year the total downtime should not be more than 5 minutes and 16 seconds.
The way 5N Network Availability is define is “The network is available for the targeted communications in 99.999% of the locations where the network is deployed and 99.999% of the time”. (from “4.4.4 Resilience and High Availability”, NGMN 5G White Paper).
Thus in a 100,000 cell network only 1 cell is allowed experience a downtime and for no longer than less than a second a day.
It should be noted that there are not many networks today that come even close to this kind of requirement. Certainly in countries with frequent long power outages and limited ancillary backup (i.e., battery and/or diesel) this could be a very costly design requirement. Networks relying on weather-sensitive microwave radios for backhaul or for mm-wave frequencies 5G coverage would be required to design in a very substantial amount of redundancy to keep such high geographical & time availability requirements
In general designing a cellular access network for this kind of 5N availability could be fairly to very costly (i.e., Capex could easily run up in several percentage points of Revenue).
One way out from a design perspective is to rely on hierarchical coverage. Thus, for example if a small cell environment is un-available (=down!) the macro-cellular network (or overlay network) continues the service although at a lower service level (i.e., lower or much lower speed compared to the primary service). As also suggested in the vision paper making use of self-healing network features and other real-time measures are expected to further increase the network infrastructure availability. This is also what one may define as Network Resilience.
Nevertheless, the “NGMN 5G White Paper” allows for operators to define the level of network availability appropriate from their own perspective (and budgets I assume).
5N Data Packet Transmission Reliability.
The 5G vision paper, defines Reliability as “… amount of sent data packets successfully delivered to a given destination, within the time constraint required by the targeted service, divided by the total number of sent data packets.”. (“4.4.5 Reliability” in “NGMN 5G White Paper”).
It should be noted that the 5N specification in particular addresses specific use cases or services of which such a reliability is required, e.g., mission critical communications and ultra-low latency service. The 5G allows for a very wide range of reliable data connection. Whether the 5N Reliability requirement will lead to substantial investments or can be managed within the overall 5G design and architectural framework, might depend on the amount of traffic requiring 5Ns.
The 5N data packet transmission reliability target would impose stricter network design. Whether this requirement would result in substantial incremental investment and cost is likely dependent on the current state of existing network infrastructure and its fundamental design.
If you have read Michael Lewis book “Flash Boys”, I will have absolutely no problem convincing you that a few milliseconds improvement in transport time (i.e., already below 20 ms) of a valuable signal (e.g., containing financial information) can be of tremendous value. It is all about optimizing transport distances, super efficient & extremely fast computing and of course ultra-high availability. The ultra-low transport and process latencies is the backbone (together with the algorithms obviously) of the high frequency trading industry that takes a market share of between 30% (EU) and 50% (US) of the total equity trading volume.
In a recent study by The Boston Consulting Group (BCG) “Uncovering Real Mobile Data Usage and Drivers of Customer Satisfaction” (Nov. 2015) study it was found that latency had a significant impact on customer video viewing satisfaction. For latencies between 75 – 100 milliseconds 72% of users reported being satisfied. The user experience satisfaction level jumped to 83% when latency was below 50 milliseconds. We have most likely all experienced and been aggravated by long call setup times (> couple of seconds) forcing us to look at the screen to confirm that a call setup (dialing) is actually in progress.
Latency and reactiveness or responsiveness matters tremendously to the customers experience and whether it is a bad, good or excellent one.
The Tactile Internet idea is an integral part of the “NGMN 5G Vision” and part of what is characterized as Extreme Real-Time Communications. It has further been worked out in detail in the ITU-T Technology Watch Report “The Tactile Internet” from August 2014.
The word “Tactile” means perceptible by touch. It closely relates to the ambition of creating a haptic experience. Where haptic means a sense of touch. Although we will learn that the Tactile Internet vision is more than a “touchy-feeling” network vision, the idea of haptic feedback in real-time (~ sub-millisecond to low millisecond regime) is very important to the idea of a Tactile Network experience (e.g., remote surgery).
The Tactile Internet is characterized by
Ultra-low latency; 1 ms and below latency (as in round-trip-time / round-trip delay).
Ultra-high availability; 99.999% availability.
Ultra-secure end-2-end communications.
Persistent very high bandwidths capability; 1 Gbps and above.
The Tactile Internet is one of the corner stones of 5G. It promises ultra-low end-2-end latencies in the order of 1 millisecond at Giga bits per second speeds and with five 9’s of availability (translating into a 500 ms per day average un-availability).
Interestingly, network predictability and variation in latency have not been receiving too much focus within the Tactile Internet work. Clearly, a high degree of predictability as well as low jitter (or latency variation), could be very desirable property of a tactile network. Possibly even more so than absolute latency in its own right. A right sized round-trip-time with imposed managed latency, meaning a controlled variation of latency, is very essential to the 5G Tactile Internet experience.
It’s 5G on speed and steroids at the same time.
Let us talk about the elephant in the room.
We can understand Tactile latency requirements in the following way;
An Action including (possible) local Processing, followed by some Transport and Remote Processing of data representing the Action, results in a Re-action again including (possible) local Processing. According with Tactile Internet Vision, the time of this whole even from Action to Re-action has to have run its cause within 1 millisecond or one thousand of a second. In many use cases this process is looped as the Re-action feeds back, resulting in another action. Note in the illustration below, Action and Re-action could take place on the same device (or locality) or could be physically separated. The processes might represent cloud-based computations or manipulations of data or data manipulations local to the device of the user as well as remote devices. It needs to be considered that the latency time scale for one direction is not at all given to be the same in the other direction (even for transport).
The simplest example is the mouse click on a internet link or URL (i.e., the Action) resulting a translation of the URL to an IP address and the loading of the resulting content on your screen (i.e., part of the process) with the final page presented on the your device display (i.e., Re-action). From the moment the URL is mouse-clicked until the content is fully presented should take no longer than 1 ms.
A more complex use case might be remote surgery. In which a surgical robot is in one location and the surgeon operator is at another location manipulating the robot through an operation. This is illustrated in the above picture. Clearly, for a remote surgical procedure to be safe (i.e., within the margins of risk of not having the possibility of any medical assisted surgery) we would require a very reliable connection (99.999% availability), sufficient bandwidth to ensure adequate video resolution as required by the remote surgeon controlling the robot, as little as possible latency allowing the feel of instantaneous (or predictable) reaction to the actions of the controller (i.e., the surgeons) and of course as little variation in the latency (i.e., jitter) allowing system or human correction of the latency (i.e., high degree of network predictability).
The first Complete Trans-Atlantic Robotic Surgery happened in 2001. Surgeons in New York (USA) remotely operated on a patient in Strasbourg, France. Some 7,000 km away or equivalent to 70 ms in round-trip-time (i.e., 14,000 km in total) for light in fiber. The total procedural delay from hand motion (action) to remote surgical response (reaction) showed up on their video screen took 155 milliseconds. From trials on pigs any delay longer than 330 ms was thought to be associated with an unacceptable degree of risk for the patient. This system then did not offer any haptic feedback to the remote surgeon. This remains the case for most (if not all) remote robotic surgical systems in option today as the latency in most remote surgical scenarios render haptic feedback less than useful. An excellent account for robotic surgery systems (including the economics) can be found at this web site “All About Robotic Surgery”. According to experienced surgeons at 175 ms (and below) a remote robotic operation is perceived (by the surgeon) as imperceptible.
It should be clear that apart from offering long-distance surgical possibilities, robotic surgical systems offers many other benefits (less invasive, higher precision, faster patient recovery, lower overall operational risks, …). In fact most robotic surgeries are done with surgeon and robot being in close proximity.
Another example of coping with lag or latency is a Predator drone pilot. The plane is a so-called unmanned combat aerial vehicle and comes at a price of ca. 4 Million US$ (in 2010) per piece. Although this aerial platform can perform missions autonomously it will typically have two pilots on the ground monitoring and possible controlling it. The typical operational latency for the Predator can be as much as 2,000 milliseconds. For takeoff and landing, where this latency is most critical, typically the control is handed to to a local crew (either in Nevada or in the country of its mission). The Predator cruise speed is between 130 and 165 km per hour. Thus within the 2 seconds lag the plane will have move approximately 100 meters (i.e., obviously critical in landing & take off scenarios). Nevertheless, a very high degree of autonomy has been build into the Predator platform that also compensates for the very large latency between plane and mission control.
Back to the Tactile Internet latency requirements;
In LTE today, the minimum latency (internal to the network) is around 12 ms without re-transmission and with pre-allocated resources. However, the normal experienced latency (again internal to the network) would be more in the order of 20 ms including 10% likelihood of retransmission and assuming scheduling (which would be normal). However, this excludes any content fetching, processing, presentation on the end-user device and the transport path beyond the operators network (i.e., somewhere in the www). Transmission outside the operator network typically between 10 and 20 ms on-top of the internal latency. The fetching, processing and presentation of content can easily add hundreds of milliseconds to the experience. Below illustrations provides a high level view of the various latency components to be considered in LTE with the transport related latencies providing the floor level to be expected;
In 5G the vision is to achieve a factor 20 better end-2-end (within the operators own network) round-trip-time compared to LTE; thus 1 millisecond.
So … what happens in 1 millisecond?
Light will have travelled ca. 200 km in fiber or 300 km in free-space. A car driving (or the fastest baseball flying) 160 km per hour will have moved 4 cm. A steel ball falling to the ground (on Earth) would have moved 5 micro meter (that’s 5 millionth of a meter). In a 1Gbps data stream, 1 ms correspond to ca. 125 Kilo Bytes worth of data. A human nerve impulse last just 1 ms (i.e., in a 100 millivolt pulse).
It should be clear that the 1 ms poses some very dramatic limitations;
The useful distance over which a tactile applications would work (if 1 ms would really be the requirements that is!) will be short ( likely a lot less than 100 km for fiber-based transport)
The air-interface (& number of control plane messages required) needs to reduce dramatically from milliseconds down to microseconds, i.e., factor 20 would require no more than 100 microseconds limiting the useful cell range).
Compute & processing requirements, in terms of latency, for UE (incl. screen, drivers, local modem, …), Base Station and Core would require a substantial overhaul (likely limiting level of tactile sophistication).
Require own controlled network infrastructure (at least a lot easier to manage latency within), avoiding any communication path leaving own network (walled garden is back with a vengeance?).
Network is the sole responsible for latency and can be made arbitrarily small (by distance and access).
Very small cells, very close to compute & processing resources, would be most likely candidates for fulfilling the tactile internet requirements.
Thus instead of moving functionality and compute up and towards the cloud data center we (might) have an opposing force that requires close proximity to the end-users application. Thus, the great promise of cloud-based economical efficiency is likely going to be dented in this scenario by requiring many more smaller data centers and maybe even micro-data centers moving closer to the access edge (i.e., cell site, aggregation site, …). Not surprisingly, Edge Cloud, Edge Data Center, Edge X is really the new Black …The curse of the edge!?
Looking at several network and compute design considerations a tactile application would require no more than 50 km (i.e., 100 km round-trip) effective round-trip distance or 0.5 ms fiber transport (including switching & routing) round-trip-time. Leaving another 0.5 ms for air-interface (in a cellular/wireless scenario), computing & processing. Furthermore, the very high degree of imposed availability (i.e., 99.999%) might likewise favor proximity between the Tactile Application and any remote Processing-Computing. Obviously,
So in all likelihood we need processing-computing as near as possible to the tactile application (at least if one believes in the 1 ms and about target).
One of the most epic (“in the Dutch coffee shop after a couple of hours category”) promises in “The Tactile Internet” vision paper is the following;
“Tomorrow, using advanced tele-diagnostic tools, it could be available anywhere, anytime; allowing remote physical examination even by palpation (examination by touch). The physician will be able to command the motion of a tele-robot at the patient’s location and receive not only audio-visual information but also critical haptic feedback.” (page 6, section 3.5).
Markus Rank et al did systematic research on the perception of delay in haptic tele-presence systems (Presence, October 2010, MIT Press) and found haptic delay detection thresholds between 30 and 55 ms. Thus haptic feedback did not appear to be sensitive to delays below 30 ms, fairly close to the lowest reported threshold of 20 ms. This combined with experienced tele-robotic surgeons assessing that below 175 ms the remote procedure starts to be perceived as imperceptible, might indicate that the 1 ms, at least for this particular use case, is extremely limiting.
The extreme case would be to have the tactile-related computing done at the radio base station assuming that the tactile use case could be restricted to the covered cell and users supported by that cell. I name this the micro-DC (or micro-cloud or more like what some might call the cloudlet concept) idea. This would be totally back to the older days with lots of compute done at the cell site (and likely kill any traditional legacy cloud-based efficiency thinking … love to use legacy and cloud in same sentence). This would limit the round-trip-time to air-interface latency and compute/processing at the base station and the device supporting the tactile application.
It is normal to talk about the round-trip-time between an action and the subsequent reaction. It is also the time it takes a data or signal to travel from a specific source to a specific destination and back again (i.e., round trip). In case of light in fiber, a 1 millisecond limit on the round-trip-time would imply that the maximum distance that can be travelled (in the fiber) between source to destination and back to the source is 200 km. Limiting the destination to be no more than 100 km away from the source. In case of substantial processing overhead (e.g., computation) the distance between source and destination requires even less than 100 km to allow for the 1 ms target.
THE HUMAN SENSES AND THE TACTILE INTERNET.
The “touchy-feely” aspect, or human sensing in general, is clearly an inspiration to the authors of “The Tactile Internet” vision as can be seen from the following quote;
“We experience interaction with a technical system as intuitive and natural only if the feedback of the system is adapted to our human reaction time. Consequently, the requirements for technical systems enabling real-time interactions depend on the participating human senses.” (page 2, Section 1).
The following human-reaction times illustration shown below is included in “The Tactile Internet” vision paper. Although it originates from Fettweis and Alamouti’s paper titled “5G: Personal Mobile Internet beyond What Cellular Did to Telephony“. It should be noted that the description of the Table is order of magnitude of human reaction times; thus, 10 ms might also be 100 ms or 1 ms and so forth and therefor, as we shall see, it would be difficult to a given reaction time wrong within such a range.
The important point here is that the human perception or senses impact very significantly the user’s experience with a given application or use case.
The responsiveness of a given system or design is incredible important for how well a service or product will be perceived by the user. The responsiveness can be defined as a relative measure against our own sense or perception of time. The measure of responsiveness is clearly not unique but depends on what senses are being used as well as the user engaged.The human mind is not fond of waiting and waiting too long causes distraction, irritation and ultimate anger after which the customer is in all likelihood lost. A very good account of considering the human mind and it senses in design specifications (and of course development) can be found in Jeff Johnson’s 2010 book “Designing with the Mind in Mind”.
The understanding of human senses and the neurophysiological reactions to those senses are important for assessing a given design criteria’s impact on the user experience. For example, designing for 1 ms or lower system reaction times when the relevant neurophysiological timescale is measured in 10s or 100s of milliseconds is likely not resulting in any noticeable (and monetizable) improvement in customer experience. Of course there can be many very good non-human reasons for wanting low or very low latencies.
While you might get the impression, from the above table above from Fettweis et al and countless Tactile Internet and 5G publications referring back to this data, that those neurophysiological reactions are natural constants, it is unfortunately not the case. Modality matters hugely. There are fairly great variations in reactions time within the same neurophysiological response category depending on the individual human under test but often also depending on the underlying experimental setup. In some instances the reaction time deduced would be fairly useless as a design criteria for anything as the detection happens unconsciously and still require the relevant part of the brain to make sense of the event.
Based on IAAF (International Athletic Association Federation) rules, an athlete is deemed to have had a false start if that athlete moves sooner than 100 milliseconds after the start signal. The neurophysiological process relevant here is the neuromuscular reaction to the sound heard (i.e., the big bang of the pistol) by the athlete. Research carried out by Paavo V. Komi et al has shown that the reaction time of a prepared (i.e., waiting for the bang!) athlete can be as low as 80 ms. This particular use case relates to the auditory reaction times and the subsequent physiological reaction. P.V. Komi et al also found a great variation in the neuromuscular reaction time to the sound (even far below the 80 ms!).
Neuromuscular reactions to unprepared events typically typically measures in several hundreds of milliseconds (up-to 700 ms) being somewhat faster if driven by auditory senses rather than vision. Note that reflex time scales are approximately 10 times faster or in the order of 80 – 100 ms.
The international Telecommunications Union (ITU) Recommendation G.114, defines for voice applications an upper acceptable one-way (i.e., its you talking you don’t want to be talked back to by yourself) delay of 150 ms. Delays below this limit would provide an acceptable degree of voice user experience in the sense that most users would not hear the delay. It should be understood that a great variation in voice delay sensitivity exist across humans. Voice conversations would be perceived as instantaneous by most below the 100 ms (thought the auditory perception would also depend on the intensity/volume of the voice being listened to).
Finally, let’s discuss human vision. Fettweis et al in my opinion mixes up several psychophysical concepts of vision and TV specifications. Alluding to 10 millisecond is the visual “reaction” time (whatever that now really means). More accurately they describe the phenomena of flicker fusion threshold which describes intermittent light stimulus (or flicker) is perceived as completely steady to an average viewer. This phenomena relates to persistence of vision where the visual system perceives multiple discrete images as a single image (both flicker and persistence of vision are well described in both by Wikipedia and in detail by Yhong-Lin Lu el al “Visual Psychophysics”). There, are other reasons why defining flicker fusion and persistence of vision as a human reaction reaction mechanism is unfortunate.
The 10 ms for vision reaction time, shown in the table above, is at the lowest limit of what researchers (see references 14, 15, 16 ..) find to be the early stages of vision can possible detect (i.e., as opposed to pure guessing ). Mary C. Potter of M.I.T.’s Dept. of Brain & Cognitive Sciences, seminal work on human perception in general and visual perception in particular shows that the human vision is capable very rapidly to make sense of pictures, and objects therein, on the timescale of 10 milliseconds (i.e., 13 ms actually is the lowest reported by Potter). From these studies it is also found that preparedness (i.e., knowing what to look for) helps the detection process although the overall detection results did not differ substantially from knowing the object of interest after the pictures were shown. Note that the setting of these visual reaction time experiments all happens in a controlled laboratory setting with the subject primed to being attentive (e.g., focus on screen with fixation cross for a given period, followed by blank screen for another shorter period, and then a sequence of pictures each presented for a (very) short time, followed again by a blank screen and finally a object name and the yes-no question whether the object was observed in the sequence of pictures). Often these experiments also includes a certain degree of training before the actual experiment took place. The relevant memory of the target object, In any case and unless re-enforced, will rapidly dissipates. in fact the shorter the viewing time, the quicker it will disappear … which might be a very healthy coping mechanism.
To call this visual reaction time of 10+ ms typical is in my opinion a bit of a stretch. It is typical for that particular experimental setup and very nicely provides important insights into the visual systems capabilities.
One of the more silly things used to demonstrate the importance of ultra-low latencies have been to time delay the video signal send to a wearer’s goggles and then throw a ball at him in the physical world … obviously, the subject will not catch the ball (might as well as thrown it at the back of his head instead). In the Tactile Internet vision paper it the following is stated; “But if a human is expecting speed, such as when manually controlling a visual scene and issuing commands that anticipate rapid response, 1-millisecond reaction time is required” (on page 3). And for the record spinning a basketball on your finger has more to do with physics than neurophysiology and human reaction times.
In more realistic settings it would appear that the (prepared) average reaction time of vision is around or below 40 ms. With this in mind, a baseball moving (when thrown by a power pitcher) at 160 km per hour (or ca. 4+ cm per ms) would take a approx. 415 ms to reach the batter (using an effective distance of 18.44 meters). Thus the batter has around 415 ms to visually process the ball coming and hit it at the right time. Given the latency involved in processing vision the ball would be at least 40 cm (@ 10 ms) closer to the batter than his latent visionary impression would imply. Assuming that the neuromuscular reaction time is around 100±20 ms, the batter would need to compensate not only for that but also for his vision process time in order to hit the ball. Based on batting statistics, clearly the brain does compensate for its internal latencies pretty well. In the paper “Human time perception and its illusions” D.M. Eaglerman that the visual system and the brain (note: visual system is an integral part of the brain) is highly adaptable in recalibrating its time perception below the sub-second level.
It is important to realize that in literature on human reaction times, there is a very wide range of numbers for supposedly similar reaction use cases and certainly a great deal of apparent contradictions (though the experimental frameworks often easily accounts for this).
The supporting data for the numbers shown in the above figure can be found via the hyperlink in the above text or in the references below.
Thus, in my opinion, also supported largely by empirical data, a good latency E2E design target for a Tactile network serving human needs, would be between 20 milliseconds and 10 milliseconds. With the latency budget covering the end user device (e.g., tablet, VR/AR goggles, IOT, …), air-interface, transport and processing (i.e., any computing, retrieval/storage, protocol handling, …). It would be unlikely to cover any connectivity out of the operator”s network unless such a connection is manageable from latency and jitter perspective though distance would count against such a strategy.
This would actually be quiet agreeable from a network perspective as the distance to data centers would be far more reasonable and likely reduce the aggressive need for many edge data centers using the below 10 ms target promoted in the Tactile Internet vision paper.
There is however one thing that we are assuming in all the above. It is assumed that the user’s local latency can be managed as well and made almost arbitrarily small (i.e., much below 1 ms). Hardly very reasonable even in the short run for human-relevant communications ecosystems (displays, goggles, drivers, etc..) as we shall see below.
For a gaming environment we would look at something like the below illustration;
Lets ignore the use case of local games (i.e., where the player only relies on his local computing environment) and focus on games that rely on a remote gaming architecture. This could either be relying on a client-server based architecture or cloud gaming architecture (e.g., typical SaaS setup). In general the the client-server based setup requires more performance of the users local environment (e.g., equipment) but also allows for more advanced latency compensating strategies enhancing the user perception of instantaneous game reactions. In the cloud game architecture, all game related computing including rendering/encoding (i.e., image synthesis) and video output generation happens in the cloud. The requirements to the end users infrastructure is modest in the cloud gaming setup. However, applying latency reduction strategies becomes much more challenging as such would require much more of the local computing environment that the cloud game architecture tries to get away from. In general the network transport related latency would be the same provide the dedicated game servers and the cloud gaming infrastructure would reside within the same premises. In Choy et al’s 2012 paper “The Brewing Storm in Cloud Gaming: A Measurement Study on Cloud to End-User Latency” , it is shown, through large scale measurements, that current commercial cloud infrastructure architecture is unable to deliver the latency performance for an acceptable (massive) multi-user experience. Partly simply due to such cloud data centers are too far away from the end user. Moreover, the traditional commercial cloud computing infrastructure is simply not optimized for online gaming requiring augmentation of stronger computing resources including GPUs and fast memory designs. Choy et al do propose to distribute the current cloud infrastructure targeting a shorter distance between end user and the relevant cloud game infrastructure. Similar to what is already happening today with content distribution networks (CDNs) being distributed more aggressively in metropolitan areas and thus closer to the end user.
A comprehensive treatment on latencies, or response time scales, in games and how these relates to user experience can be found in Kjetil Raaen’s Ph.D. thesis “Response time in games: Requirements and improvements” as well as in the comprehensive relevant literature list found in this thesis.
From the many studies (as found in Raaen’s work, the work of Mark Claypool and much cited 2002 study by Pantel et al) on gaming experience, including massive multi-user online game experience, shows that players starts to notice delay of about 100 ms of which ca. 20 ms comes from play-out and processing delay. Thus, quiet a far cry from the 1 millisecond. From the work, and not that surprising, sensitivity to gaming latency depends on the type of game played (see the work of Claypool) and how experienced a gamer is with the particular game (e.g., Pantel er al). It should also be noted that in a VR environment, you would want to the image that arrives at your visual system to be in synch with your heads movement and the directions of your vision. If there is a timing difference (or lag) between the direction of your vision and the image presented to your visual system, the user experience becomes rapidly poor causing discomfort by disorientation and confusion (possible leading to a physical reaction such as throwing up). It is also worth noting that in VR there is a substantially latency component simple from the image rendering (e.g., 60 MHz frame rate provides a new frame on average every 16.7 millisecond). Obviously chunking up the display frame rate will reduce the rendering related latency. However, several latency compensation strategies (to compensate for you head and eye movements) have been developed to cope with VR latency (e.g., time warping and prediction schemes).
Anyway, if you would be of the impression that VR is just about showing moving images on the inside of some awesome goggles … hmmm do think again and keep dreaming of 1 millisecond end-2end network centric VR delivery solutions (at least for the networks we have today). Of course 1 ms target is possible really a Proxima-Centauri shot as opposed to a just moonshot.
With a target of no more than 20 milliseconds lag or latency and taking into account the likely reaction time of the users VR system (future system!), that likely leaves no more (and likely less) than 10 milliseconds for transport and any remote server processing. Still this could allow for a data center to be 500 km (5 ms round.trip time in fiber) away from the user and allow another 5 ms for data center processing and possible routing delay along the way.
One might very well be concerned about the present Tactile Internet vision and it’s focus on network centric solutions to the very low latency target of 1 millisecond. The current vision and approach would force (fixed and mobile) network operators to add a considerable amount of data centers in order to get the physical transport time down below the 1 millisecond. This in turn drives the latest trend in telecommunication, the so-called edge data center or edge cloud. In the ultimate limit, such edge data centers (however small) might be placed at cell site locations or fixed network local exchanges or distribution cabinets.
Furthermore, the 1 millisecond as a goal might very well have very little return on user experience (UX) and substantial cost impact for telecom operators. A diligent research through academic literature and wealth of practical UX experiments indicates that this indeed might be the case.
Such a severe and restrictive target as the 1 millisecond is, it severely narrows the Tactile Internet to scenarios where sensing, acting, communication and processing happens in very close proximity of each other. In addition the restrictions to system design it imposes, further limits its relevance in my opinion. The danger is, with the expressed Tactile vision, that too little academic and industrious thinking goes into latency compensating strategies using the latest advances in machine learning, virtual reality development and computational neuroscience (to name a few areas of obvious relevance). Further network reliability and managed latency, in the sense of controlling the variation of the latency, might be of far bigger importance than latency itself below a certain limit.
So if 1 ms is no use to most men and beasts … why bother with this?
While very low latency system architectures might be of little relevance to human senses, it is of course very likely (as it is also pointed out in the Tactile Internet Vision paper) that industrial use cases could benefit from such specifications of latency, reliability and security.
For example in machine-to-machine or things-to-things communications between sensors, actuators, databases, and applications very short reaction times in the order of sub-milliseconds to low milliseconds could be relevant.
We will look at this next.
THE TACTILE INTERNET USE CASES & BUSINESS MODELS.
An open mind would hope that most of what we do strives to out perform human senses, improve how we deal with our environment and situations that are far beyond mere mortal capabilities. Alas I might have read too many Isaac Asimov novels as a kid and young adult.
In particular where 5G has its present emphasis of ultra-high frequencies (i.e., ultra small cells), ultra-wide spectral bandwidth (i.e., lots of Gbps) together with the current vision of the Tactile Internet (ultra-low latencies, ultra-high reliability and ultra-high security), seem to be screaming for being applied to Industrial facilities, logistic warehouses, campus solutions, stadiums, shopping malls, tele-, edge-cloud, networked robotics, etc… In other words, wherever we have a happy mix of sensors, actuators, processors, storage, databases and software based solutions across a relative confined area, 5G and the Tactile Internet vision appears to be a possible fit and opportunity.
In the following it is important to remember;
1 ms round-trip time ~ 100 km (in fiber) to 150 km (in free space) in 1-way distance from the relevant action if only transport distance mattered to the latency budget.
Considering the total latency budget for a 1 ms Tactile application the transport distance is likely to be no more than 20 – 50 km or less (i.e., right at the RAN edge).
5G will bring lower latency, compared to an even optimized LTE system, that in a similar setup as the above described for Ocado, could further increase the performance. Obviously very high network reliability promised by 5G of such a logistic system needs to be very high to reduce the risk of disruption and subsequent customer dissatisfaction of late (or no) delivery as well as the exposure to grocery stock turning bad.
This all done within the confines of a warehouse building.
ROBOTICS AND TACTILE CONDITIONS
First of all lets limit the Robotics discussion to use cases related to networked robots. After all if the robot doesn’t need a network (pretty cool) it pretty much a singleton and not so relevant for the Tactile Internet discussion. In the following I am using the word Cloud in a fairly loose way and means any form of computing center resources either dedicated or virtualized. The cloud could reside near the networked robotic systems as well as far away depending on the overall system requirements to timing and delay (e.g., that might also depend on the level of robotic autonomy).
Getting networked robots to work well we need to solve a host of technical challenges, such as
Jitter (i.e., variation of latency).
Robot-2-ROS (i.e., general robotics operations system).
Power budget (e.g., power limitations, re-charging).
Sensor & actuator fusion (e.g., consolidate & align data from distributed sources for example sensor-actuator network).
Autonomy vs human control.
Machine learning / machine intelligence.
Safety (e.g., human and non-human).
Security (e.g., against cyber threats).
The network connection-part of the networked robotics system can be either wireless, wired, or a combination of wired & wireless. Connectivity could be either to a local computing cloud or data center, to an external cloud (on the internet) or a combination of internal computing for control and management for applications requiring very low-latency very-low jitter communications and external cloud for backup and latency-jitter uncritical applications and use cases.
For connection types we have Wired (e.g., LAN), Wireless (e.g., WLAN) and Cellular (e.g., LTE, 5G). There are (at least) three levels of connectivity we need to consider; inter-robot communications, robot-to-cloud communications (or operations and control systems residing in Frontend-Cloud or computing center), and possible Frontend-Cloud to Backend-Cloud (e..g, for backup, storage and latency-insensitive operations and control systems). Obviously, there might not be a need for a split in Frontend and Backend Clouds and pending on the use case requirements could be one and the same. Robots can be either stationary or mobile with a need for inter-robot communications or simply robot-cloud communications.
Various networked robot connectivity architectures are illustrated below;
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
“Neurophysiology: A Conceptual Approach” by Roger Carpenter & Benjamin Reddi (Fifth Edition, 2013 CRC Press).Definitely a very worthy read by anyone who want to understand the underlying principles of sensory functions and basic neural mechanisms.
“Designing with the Mind in Mind” by Jeff Johnson (2010, Morgan Kaufmann). Lots of cool information of how to design a meaningful user interface and of basic user expirence principles worth thinking about.
“World first in radio design” by Cambridge Consultants. Describing the work Cambridge Consultants did with Ocado (UK-based) to design the worlds most automated technologically advanced warehouse based on 4G connected robotics. Please do see the video enclosed in page.
“Ocado: next-generation warehouse automation” by Cambridge Consultants.
After 3G came 4G. After 4G comes 5G. After 5G comes 6G. The Shrivatsa of Technology.
This blog (over the next months a series of Blogs dedicated to 5G), “5G Economics – An Introduction”, has been a very long undertaking. In the making since 2014. Adding and then deleting as I change my opinion and then changed it again. The NGNM Alliance “NGMN 5G White Paper” (here after the NGMN whitepaper) by Rachid El Hattachi & Javan Erfanian has been both a source of great visionary inspiration as well as a source of great worry when it comes to the economical viability of their vision. Some of the 5G ideas and aspirations are truly moonshot in nature and would make the Singularity University very proud.
So what is the 5G Vision?
“5G is an end-to-end ecosystem to enable a fully mobile and connected society. It empowers value creation towards customers and partners, through existing and emerging use cases, delivered with consistent experience, and enabled by sustainable business models.” (NGMN 5G Vision, NGMN 5G whitepaper).
The NGMN 5G vision is not only limited to enhancement of the radio/air interface (although it is the biggest cost & customer experience factor). 5G seeks to capture the complete end-2-end telecommunications system architecture and its performance specifications. This is an important difference from past focus on primarily air interface improvements (e.g., 3G, HSPA, LTE, LTE-adv) and relative modest evolutionary changes to the core network architectural improvements (PS CN, EPC). In particular, the 5G vision provides architectural guidance on the structural separation of hardware and software. Furthermore, it utilizes the latest development in software defined telecommunications functionality enabled by cloudification and virtualization concepts known from modern state-of-the art data centers. The NGMN 5G vision most likely have accepted more innovation risk than in the past as well as being substantially more ambitious in both its specifications and the associated benefits.
“To boldly go where no man has gone before”
In the following, I encourage the reader to always keep in the back of your mind; “It is far easier to criticize somebody’s vision, than it is to come with the vision yourself”. I have tons of respect for the hard and intense development work, that so far have been channeled into making the original 5G vision into a deployable technology that will contribute meaningfully to customer experience and the telecommunications industry.
For much of the expressed concerns in this blog and in other critiques, it is not that those concerns have not been considered in the NGMN whitepaper and 5G vision, but more that those points are not getting much attention.
The cellular “singularity”, 5G that is, is supposed to hit us by 2020. In only four years. Americans and maybe others, taking names & definitions fairly lightly, might already have “5G” ( a l’Americaine) in a couple of years before the real thing will be around.
The 5G Vision is a source of great inspiration. The 5G vision will (and is) requiring a lot of innovation efforts, research & development to actually deliver on what for most parts are very challenging improvements over LTE.
My own main points of concern are in particular towards the following areas;
Obsession with very high sustainable connection throughputs (> 1 Gbps).
Extremely low latencies (1 ms and below).
Too little (to none) focus on controlling latency variation (e.g., jitter), which might be of even greater importance than very low latency (<<10 ms) in its own right. I term this network predictability.
Too strong focus on frequencies above 3 GHz in general and in particular the millimeter wave range of 30 GHz to 300 GHz.
Backhaul & backbone transport transformation needed to support the 5G quantum leap in performance has been largely ignored.
Relative weak on fixed – mobile convergence.
Not so much whether some of the above points are important or not .. they are of course important. Rather it is a question of whether the prioritization and focus is right. A question of channeling more efforts into very important (IMO) key 5G success factors, e.g., transport, convergence and designing 5G for the best user experience (and infinitely faster throughput per user is not the answer) ensuring the technology to be relevant for all customers and not only the ones who happens to be within coverage of a smallest cell.
Not surprisingly the 5G vision is a very mobile system centric. There is too little attention to fixed-mobile convergence and the transport solutions (backhaul & backbone) that will enable the very high air-interface throughputs to be carried through the telecoms network. This is also not very surprising as most mobile folks, historically did not have to worry too much about transport at least in mature advanced markets (i.e., the solutions needed was there without innovation an R&D efforts).
However, this is a problem. The required transport upgrade to support the 5G promises is likely to be very costly. The technology economics and affordability aspects of what is proposed is still very much work in progress. It is speculated that new business models and use cases will be enabled by 5G. So far little has been done in quantifying those opportunities and see whether those can justify some of the incremental cost that surely operators will incur as the deploy 5G.
CELLULAR CAPACITY … IT WORKS FOR 5G TOO!
To create more cellular capacity measured in throughput is easy or can be made so with a bit of approximations. “All” we need is an amount of frequency bandwidth Hz, an air-interface technology that allow us to efficiently carry a certain amount of information in bits per second per unit bandwidth per capacity unit (i.e., we call this spectral efficiency) and a number of capacity units or multipliers which for a cellular network is the radio cell. The most challenging parameter in this game is the spectral efficiency as it is governed by the laws of physics with a hard limit (actually silly me … bandwidth and capacity units are obviously as well), while a much greater degree of freedom governs the amount of bandwidth and of course the number of cells.
Spectral efficiency is given by the so-called Shannon’s Law (for the studious inclined I recommend to study his 1948 paper “A Mathematical Theory of Communications”). The consensus is that we are very close to the Shannon Limit in terms of spectral efficiency (in terms of bits per second per Hz) of the cellular air-interface itself. Thus we are dealing with diminishing returns of what can be gained by further improving error correction, coding and single-input single-output (SISO) antenna technology.
I could throw more bandwidth at the capacity problem (i.e., the reason for the infatuation with the millimeter wave frequency range as there really is a lot available up there at 30+ GHz) and of course build a lot more cell sites or capacity multipliers (i.e., definitely not very economical unless it results in a net positive margin). Of course I could (and most likely will if I had a lot of money) do both.
I could also try to be smart about the spectral efficiency and Shannon’s law. If I could reduce the need for or even avoid building more capacity multipliers or cell sites, by increasing my antenna system complexity it is likely resulting in very favorable economics. It turns out that multiple antennas acts as a multiplier (simplistic put) for the spectral efficiency compared to a simple single (or legacy) antenna system. Thus, the way to improve the spectral efficiency inevitable leads us to substantially more complex antenna technologies (e.g., higher order MiMo, massive MiMo, etc…).
Building new cell sites or capacity multiplier should always be the last resort as it is most likely the least economical option available to boost capacity.
Thus we should be committing increasingly more bandwidth (i.e., 100s – 1000s of Mhz and beyond) assuming it is available (i.e, if not we are back to adding antenna complexity and more cell sites). The need for very large bandwidths, in comparison with what is deployed in today’s cellular systems, automatically forces the choices into high frequency ranges, i.e., >3 GHz and into the millimeter wave range of above 30 GHz. The higher frequency band leads in inevitably to limited coverage and a high to massive demand for small cell deployment.
Yes! It’s a catch 22 if there ever was one. The higher carrier frequency increases the likelihood of more available bandwidth. higher carrier frequency also results in a reduced the size of our advanced complex antenna system (which is good). Both boost capacity to no end. However, my coverage area where I have engineered the capacity boost reduces approx. with the square of the carrier frequency.
Clearly, ubiquitous 5G coverage at those high frequencies (i.e., >3 GHz) would be a very silly endeavor (to put it nicely) and very un-economical.
5G, as long as the main frequency deployed is in the high or very high frequency regime, would remain a niche technology. Irrelevant to a large proportion of customers and use cases.
5G needs to be macro cellular focused to become relevant for all customers and economically beneficial to most use cases.
THE CURIOUS CASE OF LATENCY.
The first time I heard about the 5G 1 ms latency target (communicated with a straight face and lots of passion) was to ROFL. Not a really mature reaction (mea culpa) and agreed, many might have had the same reaction when J.F. Kennedy announced to put a man on the moon and safely back on Earth within 10 years. So my apologies for having had a good laugh (likely not the last to laugh though in this matter).
In Europe, the average LTE latency is around 41±9 milliseconds including pinging an external (to the network) server but does not for example include the additional time it takes to load a web page or start a video stream. The (super) low latency (1 ms and below) poses other challenges but at least relevant to the air-interface and a reasonable justification to work on a new air-interface (apart from studying channel models in the higher frequency regime). The best latency, internal to the mobile network itself, you can hope to get out of “normal” LTE as it is commercially deployed is slightly below 20 ms (without considering re-transmission). For pre-allocated LTE this can further be reduced towards the 10 ms (without considering re-transmission which adds at least 8 ms). In 1 ms light travels ca. 200 km (in optical fiber). To support use cases requiring 1 ms End-2-End latency, all transport & processing would have to be kept inside the operators network. Clearly, the physical transport path to the location, where processing of the transported data would occur, would need to be very short to guaranty 1 ms. The relative 5G latency improvement over LTE would need to be (much) better than 10 (LTE pre-allocated) to 20 times (scheduled “normal” LTE), ignoring re-transmission (which would only make the challenge bigger.
An example. Say that 5G standardization folks gets the latency down to 0.5 ms (vs the ~ 20 – 10 ms today), the 5G processing node (i.e., Data Center) cannot be more than 50 km away from the 5G-radio cell (i..e, it takes light ca. 0.5 ms travel 100 km in fiber). This latency (budget) challenge has led the Telco industry to talk about the need for so-called edge computing and the need for edge data centers to provide the 5G promise of very low latencies. Remember this is opposing the past Telco trend of increasing centralization of computing & data processing resources. Moreover, it is bound to lead to incremental cost. Thus, show me the revenues.
There is no doubt that small, smaller and smallest 5G cells will be essential for providing the very lowest latencies and the smallness is coming for “free” given the very high frequencies planned for 5G. The cell environment of a small cell is more ideal than a macro-cellular harsh environment. Thus minimizing the likelihood of re-transmission events. And distances are shorter which helps as well.
I believe that converged telecommunications operators, are in a better position (particular compared to mobile only operations) to leverage existing fixed infrastructure for a 5G architecture relying on edge data centers to provide very low latencies. However, this will not come for free and without incremental costs.
End-2-End latency in the order of 20 ms are very important for a solid high quality VR user experience. However, to meet this kind of performance figure the VR content needs to be within the confines for the operator’s own network boundaries.
End-2-End (E2E) latencies of less than 100 ms would in general be perceived as instantaneous for normal internet consumption (e.g., social media, browsing, …). However that this still implies that operators will have to focus on developing internal to their network’s latencies far below the over-all 100 ms target and that due to externalities might try to get content inside their networks (and into their own data centers).
A 10-ms latency target, while much less moonshot, would be a far more economical target to strive for and might avoid substantial incremental cost of edge computing center deployments. It also resonates well with the 20 ms mentioned above, required for a great VR experience (leaving some computing and process overhead).
The 1-ms vision could be kept for use cases involving very short distances, highly ideal radio environment and with compute pretty much sitting on top of the whatever needs this performance, e.g., industrial plants, logistic / warehousing, …
Finally, the targeted extreme 5G speeds will require very substantial bandwidths. Such large bandwidths are readily available in the high frequency ranges (i.e., >3 GHz). The high frequency domain makes a lot of 5G technology challenges easier to cope with. Thus cell ranges will be (very) limited in comparison to macro cellular ones, e.g., Barclays Equity Research projects 10x times more cells will be required for 5G (10x!). 5G coverage will not match that of the macro cellular (LTE) network. In which case 5G will remain niche. With a lot less relevance to consumers. Obviously, 5G will have to jump the speed divide (a very substantial divide) to the macro cellular network to become relevant to the mass market. Little thinking appears to be spend on this challenge currently.
THE VERY FINE ART OF DETECTING MYTH & BALONEY.
Carl Sagan, in his great article The Fine Art of Baloney Detection, states that one should “Try not to get overly attached to a hypothesis just because it’s yours.”. Although Carl Sagan starts out discussing the nature of religious belief and the expectations of an afterlife, much of his “Baloney Detection Kit” applies equally well to science & technology. In particular towards our expert expectations towards consumerism and its most likely demand. After all, isn’t Technology in some respects our new modern day religion?
Some might have the impression that expectations towards 5G, is the equivalent of a belief in an afterlife or maybe more accurately resurrection of the Telco business model to its past glory. It is almost like a cosmic event, where after entropy death, the big bang gives birth to new, and supposedly unique (& exclusive) to our Telco industry, revenue streams that will make all alright (again). There clearly is some hype involved in current expectations towards 5G, although the term still has to enter the Gartner hype cycle report (maybe 2017 will be the year?).
The cynic (mea culpa) might say that it is in-evitable that there will be a 5G after 4G (that came after 3G (that came after 2G)). We also would expect 5G to be (a lot) better than 4G (that was better than 3G, etc..).
Well … Better for who? … Better for Telcos? Better for Suppliers? Better revenues? Their Shareholders? Better for our Consumers? Better for our Society? Better for (engineering) job security? … Better for Everyone and Everything? Wow! Right? … What does better mean?
Better speed … Yes! … Actually the 5G vision gives me insanely better speeds than LTE does today.
Better latency … Internal to the operator’s own network Yes! … Not per default noticeable for most consumer use cases relying on the externalities of the internet.
Better coverage … well if operators can afford to provide 100% 5G coverage then certainly Yes! Consumers would benefit even at a persistent 50 Mbps level.
Better availability …I don’t really think that Network Availability is a problem for the general consumer where there is coverage (at least not in mature markets, Myanmar absolutely … but that’s an infrastructure problem rather than a cellular standard one!) … Whether 100% availability is noticeable or not will depend a lot on the starting point.
Better (in the sense of more) revenues … Work in Progress!
Better margins … Only if incremental 5G cost to incremental 5G revenue is positive.
5G vision is flawed and not the huge advance in global connectivity as advertised.
The data rates promised by 5G will not be sufficiently valued by the users.
The envisioned 5G capacity demand will not be needed.
Most operators can simply not afford the cost required to realize 5G.
Technology advances are in-sufficient to realize the 5G vision.
Consistent connectivity is the more important aim of a 5G technology.
I recommend all to read William Webb’s well written and even better argued book. It is one for the first more official critiques of the 5G Vision. Some of the points certainly should have us pause and maybe even re-evaluate 5G priorities. If anything, it helps to sharpen 5G arguments.
Despite William Webb”s critique of 5G, one need to realize that a powerful technology vision of what 5G could be, even if very moonshot, does leapfrog innovation, needed to take a given technology too a substantially higher level, than what might otherwise be the case. If the 5G whitepaper by Rachid El Hattachi & Javan Erfanian had “just” been about better & consistent coverage, we would not have had the same technology progress independent of whether the ultimate 5G end game is completely reachable or not. Moreover, to be fair to the NGMN whitepaper, it is not that the whitepaper does not consider consistent connectivity, it very much does. It is more a matter of where lies the main attention of the industry at this moment. That attention is not on consistent connectivity but much more on niche use cases (i.e., ultra high bandwidth at ultra low latencies).
Another, very worthy 5G analysis, also from 2016, is the Barclays Equity Research “5G – A new Dawn” (September 2016) paper. The Barclays 5G analysis concludes ;
Mobile operator’s will need 10x more sites over the next 5 to 10 years driven by 5G demand.
There will be a strong demand for 5G high capacity service.
The upfront cost for 5G will be very substantial.
The cost of data capacity (i.e., Euro per GB) will fall approx. a factor 13 between LTE and 5G (note: this is “a bit” of a economic problem when capacity is supposed to increase a factor 50).
Sub-scale Telcos, including mobile-only operations, may not be able to afford 5G (note: this point, if true, should make the industry very alert towards regulatory actions).
Having a modernized super-scalable fixed broadband transport network likely to be a 5G King Maker (note: Its going to be great to be an incumbent again).
To the casual observer, it might appear that Barclays is in strong opposition to William Webb’s 5G view. However, maybe that is not completely so.
If it is true, that only very few Telco’s, primarily modernized incumbent fixed-mobile Telco’s, can afford to build 5G networks, one might argue that the 5G Vision is “somewhat” flawed economically. The root cause for this assumed economical flaw (according with Barclays, although they do not point out it is a flaw!) clearly is the very high 5G speeds, assumed to be demanded by the user. Resulting in massive increase in network densification and need for radically modernized & re-engineered transport networks to cope with this kind of demand.
Barclays assessments are fairly consistent with the illustration shown below of the likely technology cost impact, showing the challenges a 5G deployment might have;
Some of the possible operational cost improvements in IT, Platforms and Core shown in the above illustration arises from the natural evolving architectural simplifications and automation strategies expected to be in place by the time of the 5G launch. However, the expected huge increase in small cells are the root cause of most of the capital and operational cost pressures expected to arise with 5G. Depending on the original state of the telecommunications infrastructure (e.g., cloudification, virtualization,…), degree of transport modernization (e.g., fiberization), and business model (e.g., degree of digital transformation), the 5G economical impact can be relative modest (albeit momentarily painful) to brutal (i.e., little chance of financial return on investment). As discussed in the Barclays “5G – A new dawn” paper.
Furthermore, if the relative cost of delivering a 5G Byte is 13 – 14 times lower than an LTE Byte, and the 5G capacity demand is 50 times higher than LTE, the economics doesn’t work out very well. So if I can produce a 5G Byte at 1/14th of an LTE Byte, but my 5G Byte demand is 50x higher than in LTE, I could (simplistically) end up with more than 3x more absolute cost for 5G. That’s really Ugly! Although if Barclays are correct in the factor 10 higher number of 5G sites, then a (relevant) cost increase of factor 3 doesn’t seem completely unrealistic. Of course Barclays could be wrong! Unfortunately, an assessment of the incremental revenue potential has yet to be provided. If the price for a 5G Byte could be in excess of a factor 3 of an LTE Byte … all would be cool!
If there is something to be worried about, I would worry much more about the Barclays 5G analysis than the challenges of William Webb (although certainly somehow intertwined).
What is the 5G market potential in terms of connections?
Caution! Above global mobile connection forecast is likely to change many time as we approaches commercial launch and get much better impression of the 5G launch strategies of the various important players in the Telco Industry. In my own opinion, if 5G will be launched primarily in the mm-wave bands around and above 30 GHz, I would not expect to see a very aggressive 5G uptake. Possible a lot less than the above (with the danger of putting myself in the category of badly wrong forecasts of the future). If 5G would be deployed as an overlay to existing macro-cellular networks … hmmm who knows! maybe above would be a very pessimistic view of 5G uptake?
THE 5G PROMISES (WHAT OTHERS MIGHT CALL A VISION).
Let’s start with the 5G technology vision as being presented by NGMN and GSMA.
Note: ca. 5 minutes of service unavailability per year. If counted on active usage hours this would be less than 2.5 minutes per year per customer or less than 1/2 second per day per customer.
7.Perception of 100% coverage.
Note: In 2015 report from European Commission, “Broadband Coverage in Europe 2015”, for EU28, 86% of households had access to LTE overall. However, only 36% of EU28 rural households had access to LTE in 2015.
8.90% energy reduction of current network-related energy consumption.
Note: Approx. 1% of a European Mobile Operator’s total Opex.
9.Up-to 10 years battery life for low-power Internet of Things 5G devices.
The 5G whitepaper also discusses new business models and business opportunities for the Telco industry. However, there is little clarity on what would be the relevant 5G business targets. In other words, what would 5G as a technology bring, in additional Revenues, in Churn reduction, Capex & Opex (absolute) Efficiencies, etc…
More concrete and tangible economical requirements are badly required in the 5G discussion. Without it, is difficult to see how Technology can ensure that the 5G system that will be developed is also will be relevant for the business challenges in 2020 and beyond.
Today an average European Mobile operator spends approx. 40 Euro in Total Cost of Ownership (TCO) per customer per anno on network technology (and slightly less on average per connection). Assuming a capital annualization rate of 5 years and about 15% of its Opex relates to Technology (excluding personnel cost).
The 40 Euro TCO per customer per anno sustains today an average LTE EU28 customer experience of 31±9 Mbps downlink speed @ 41±9 ms (i.e., based on OpenSignal database with data as of 23 December 2016). Of course this also provides for 3G/HSPA network sustenance and what remains of the 2G network.
Thus, we might have a 5G TCO ceiling at least without additional revenue. The maximum 5G technology cost per average speed (in downlink) of 1 – 10 Gbps @ 10 ms should not be more than 40 Euro TCO per customer per anno (i.e, and preferably a lot less at the time we eventually will launch 5G in 2020).
Thus, our mantra when developing the 5G system should be:
5G should not add additional absolute cost burden to the Telecom P&L.
and also begs the question of proposing some economical requirements to partner up with the technology goals.
5G ECONOMIC REQUIREMENTS (TO BE CONSIDERED).
5G should provide new revenue opportunities in excess of 20% of access based revenue (e.g., Europe mobile access based revenue streams by 2021 expected to be in the order of 160±20 Billion Euro; thus the 5G target for Europe should be to add an opportunity of ca. 30±5 Billion in new non-access based revenues).
5G should not add to Technology TCO while delivering up-to 10 Gbps @ 10 ms (with a floor level of 1 Gbps) in urban areas.
5G focus on delivering macro-cellular customer experience at minimum 50 Mbps @ maximum 10 ms.
5G should target 20% reduction of Technology TCO while delivering up-to 10 Gbps @ 10 ms (min. 1 Gbps).
5G should keep pursuing better spectral efficiency (i.e., Mbps/MHz/cell) not only through means antennas designs, e.g., n-order MiMo and Massive-MiMo, that are largely independent of the air-interface (i.e., works as well with LTE).
Target at least 20% 5G device penetration within first 2 years of commercial launch (note: only after 20% penetration does the technology efficiency become noticeable).
In order not to increment the total technology TCO, we would at the very least need to avoid adding additional physical assets or infrastructure to the existing network infrastructure. Unless such addition provide a net removal of other physical assets and thus associated cost. This is in the current high frequency, and resulting demand for huge amount of small cells, going to be very challenging but would be less so by focusing more on macro cellular exploitation of 5G.
Thus, there need to be a goal to also overlay 5G on our existing macro-cellular network. Rather than primarily focus on small, smaller and smallest cells. Similar to what have been done for LT and was a much more challenge with UMTS (i.e., due to optimum cellular grid mismatch between the 2G voice-based and the 3G more data-centric higher frequency network).
What is the cost reference that should be kept in mind?
As shown below, the pre-5G technology cost is largely driven by access cost related to the number of deployed sites in a given network and the backhaul transmission.
Adding more sites, macro-cellular or a high number of small cells, will increase Opex and add not only a higher momentary Capex demand, but also burden future cash requirements. Unless equivalent cost can removed by the 5G addition.
Obviously, if adding additional physical assets leads to verifiable incremental margin, then accepting incremental technology cost might be perfectly okay (let”s avoid being radical financial controllers).
Though its always wise to remember;
Cost committed is a certainty, incremental revenue is not.
NAUGHTY … IMAGINE A 5G MACRO CELLULAR NETWORK (OHH JE!).
From the NGMN whitepaper, it is clear that 5G is supposed to be served everywhere (albeit at very different quality levels) and not only in dense urban areas. Given the economical constraints (considered very lightly in the NGMN whitepaper) it is obvious that 5G would be available across operators existing macro-cellular networks and thus also in the existing macro cellular spectrum regime. Not that this gets a lot of attention.
In the following, I am proposing a 5G macro cellular overlay network providing a 1 Gbps persistent connection enabled by massive MiMo antenna systems. This though experiment is somewhat at odds with the NGMN whitepaper where their 50 Mbps promise might be more appropriate. Due to the relative high frequency range in this example, massive MiMo might still be practical as a deployment option.
If you follow all the 5G news, particular on 5G trials in US and Europe, you easily could get the impression that mm-wave frequencies (e.g., 30 GHz up-to 300 GHz) are the new black.
There is the notion that;
“Extremely high frequencies means extremely fast 5G speeds”
which is baloney! It is the extremely large bandwidth, readily available in the extremely high frequency bands, that make for extremely fast 5G (and LTE of course) speeds.
We can have GHz bandwidths instead of MHz (i.e, 1,000x) to play with! … How extremely cool is that not? We totally can suck at fundamental spectral efficiency and still get out extremely high throughputs for the consumers data consumption.
While this mm-wave frequency range is very cool, from an engineering perspective and for sure academically as well, it is also extremely non-matching our existing macro-cellular infrastructure with its 700 to 2.6 GHz working frequency range. Most mobile networks in Europe have been build on a 900 or 1800 MHz fundamental grid, with fill in from UMTS 2100 MHz coverage and capacity requirements.
Being a bit of a party pooper, I asked whether it wouldn’t be cool (maybe not to the extreme … but still) to deploy 5G as an overlay on our existing (macro) cellular network? Would it not be economically more relevant to boost the customer experience across our macro-cellular networks, that actually serves our customers today? As opposed to augment the existing LTE network with ultra hot zones of extreme speeds and possible also an extreme number of small cells.
If 5G would remain an above 3 GHz technology, it would be largely irrelevant to the mass market and most use cases.
A 5G MACRO CELLULAR THOUGHT EXAMPLE.
So let’s be (a bit) naughty and assume we can free up 20MHz @ 1800 MHz. After all, mobile operators tend to have a lot of this particular spectrum anyway. They might also re-purpose 3G/LTE 2.1 GHz spectrum (possibly easier than 1800 MHz pending overall LTE demand).
In the following, I am ignoring that whatever benefits I get out of deploying higher-order MiMo or massive MiMo (mMiMo) antenna systems, will work (almost) equally well for LTE as it will for 5G (all other things being equal).
Remember we are after
A lot more speed. At least 1 Gbps sustainable user throughput (in the downlink).
Ultra-responsiveness with latencies from 10 ms and down (E2E).
No worse 5G coverage than with LTE (at same frequency).
Of course if you happen to be a NGMN whitepaper purist, you will now tell me that I my ambition should only be to provide sustainable 50 Mbps per user connection. It is nevertheless an interesting thought exercise to explore whether residential areas could be served, by the existing macro cellular network, with a much higher consistent throughput than 50 Mbps that might ultimately be covered by LTE rather than needing to go to 5G. Anywhere both Rachid El Hattachi and Jarvan Erfenian knew well enough to hedge their 5G speed vision against the reality of economics and statistical fluctuation.
and I really don’t care about the 1,000x (LTE) bandwidth per unit area promise!
Why? The 1,000x promise It is fairly trivial promise. To achieve it, I simply need a high enough frequency and a large enough bandwidth (and those two as pointed out goes nicely hand in hand). Take a 100 meter 5G-cell range versus a 1 km LTE-cell range. The 5G-cell is 100 times smaller in coverage area and with 10x more 5G spectral bandwidth than for LTE (e.g., 200 MHz 5G vs 20 MHz LTE), I would have the factor 1,000 in throughput bandwidth per unit area. This without having to assume mMiMo that I could also choose to use for LTE with pretty much same effect.
Detour to the cool world of Academia: University of Bristol published recently (March 2016) a 5G spectral efficiency of ca. 80 Mbps/MHz in a 20 MHz channel. This is about 12 times higher than state of art LTE spectral efficiency. Their base station antenna system was based on so-called massive MiMo (mMiMo) with 128 antenna elements, supporting 12 users in the cell as approx. 1.6 Gbps (i.e., 20 MHz x 80 Mbps/MHz). The proof of concept system operated 3.5 GHz and in TDD mode (note: mMiMo does not scale as well for FDD and pose in general more challenges in terms of spectral efficiency). National Instruments provides a very nice overview of 5G MMiMo systems in their whitepaper “5G Massive MiMo Testbed: From Theory to Reality”.
A picture of the antenna system is shown below;
Figure above: One of the World’s First Real-Time massive MIMO Testbeds–Created at Lund University. Source: “5G Massive MiMo (mMiMo) Testbed: From Theory to Reality” (June 2016).
For a good read and background on advanced MiMo antenna systems I recommend Chockalingam & Sundar Rajan’s book on “Large MiMo Systems” (Cambridge University Press, 2014). Though there are many excellent accounts of simple MiMo, higher-order MiMo, massive MiMo, Multi-user MiMo antenna systems and the fundamentals thereof.
Back to naughty (i.e., my 5G macro cellular network);
So let’s just assume that the above mMiMO system, for our 5G macro-cellular network,
and keeping in mind that FDD mMiMo performance tends to be lower than TDD all else being equal.
will, in due time, be available for 5G with a channel of at least 20 MHz @ 1800MHz. And at a form factor that can be integrated well with existing macro cellular design without incremental TCO.
This is a very (VERY!) big assumption. Requirements of substantially more antenna space for massive MiMo systems, at normal cellular frequency ranges, are likely to result. Structural integrity of site designs would have to be checked and possibly be re-enforced to allow for the advanced antenna system, contributing to both additional capital cost and possible incremental tower/site lease.
So we have (in theory) a 5G macro-cellular overlay network with at least cell speeds of 1+Gbps, which is ca. 10 – 20 times that of today’s LTE networks cell performance (not utilizing massive MiMo!). If I have more 5G spectrum available, the performance would increase linearly (and a bit) accordingly.
mMiMo designed for TDD, but works at some performance penalty for FDD.
mMiMo will really be deployable at low total cost of ownership (i.e., it is not enough that the antenna system itself is low cost!).
mMiMo performance leap frog comes at the price of high computational complexity (e.g., should be factored into the deployment cost).
mMiMo relies on distributed processing algorithms which at this scale is relative un-exploited territory (i.e., should be factored into the deployment cost).
But wait a minute! I might (naively) theorize away additional operational cost of the active electronics and antenna systems on the 5G cell site (overlaid on legacy already present!). I might further assume that the Capex of the 5G radio & antenna system can be financed within the regular modernization budget (assuming such a budget exists). But … But surely our access and core transport networks have not been scaled for a factor 10 – 20 (and possibly a lot more than that) in crease in throughput per active customer?
No it has not! Really Not!
Though some modernized converged Telcos might be a lot better positioned for thefixed broadband transformation required to sustain the 5G speed promise.
For most mobile operators, it is highly likely that substantial re-design and investments of transport networks will have to be made in order to support the 5G target performance increase above and beyond LTE.
Definitely a lot more on this topic in a subsequent Blog.
ON THE 5G PROMISES.
Lets briefly examine the 8 above 5G promises or visionary statements and how these impact the underlying economics. As this is an introductory chapter, the deeper dive and analysis will be referred to subsequent chapters.
NEED FOR SPEED.
PROMISE 1: From 1 to 10 Gbps in actual experienced 5G speed per connected device (at a max. of 10 ms round-trip time).
PROMISE 2: Minimum of 50 Mbps per user connection everywhere (at a max. of 10 ms round-trip time).
PROMISE 3: Thousand times more bandwidth per unit area (compared to LTE).
Before anything else, it would be appropriate to ask a couple of questions;
“Do I need this speed?” (The expert answer if you are living inside the Telecom bubble is obvious! Yes Yes Yes ….Customer will not know they need it until they have it! …).
“that kind of sustainable speed for what?” (Telekom bubble answer would be! Lots of useful things! … much better video experience, 4K, 8K, 32K –> fully emerged holographic VR experience … Lots!)
“am I willing to pay extra for this vast improvement in my experience?” (Telekom bubble answer would be … ahem … that’s really a business model question and lets just have marketing deal with that later).
What is true however is:
My objective measurable 5G customer experience, assuming the speed-coverage-reliability promise is delivered, will quantum leap to un-imaginable levels (in terms of objectively measured performance increase).
Maybe more importantly, will the 5G customer experience from the very high speed and very low latency really be noticeable to the customer? (i.e, the subjective or perceived customer experience dimension).
Let’s ponder on this!
In Europe end of 2016, the urban LTE speed and latency user experience per connection would of course depend on which network the customer would be (not all being equal);
In 2016 on average in Europe an urban LTE user, experienced a DL speed of 31±9 Mbps, UL speed of 9±2 Mbps and latency around 41±9 milliseconds. Keep in mind that OpenSignal is likely to be closer to the real user’s smartphone OTT experience, as it pings a server external to the MNOs network. It should also be noted that although the OpenSignal measure might be closer to the real customer experience, it still does not provide the full experience from for example page load or video stream initialization and start.
The 31 Mbps urban LTE user experience throughput provides for a very good video streaming experience at 1080p (e.g., full high definition video) even on a large TV screen. Even a 4K video stream (15 – 32 Mbps) might work well, provided the connection stability is good and that you have the screen to appreciate the higher resolution (i.e., a lot bigger than your 5” iPhone 7 Plus). You are unlikely to see the slightest difference on your mobile device between the 1080p (9 Mbps) and 480p (1.0 – 2.3 Mbps) unless you are healthy young and/or with a high visual acuity which is usually reserved for the healthy & young.
With 5G, the DL speed is targeted to be at least 1 Gbps and could be as high as 10 Gbps, all delivered within a round trip delay of maximum 10 milliseconds.
5G target by launch (in 2020) is to deliver at least 30+ times more real experienced bandwidth (in the DL) compared to what an average LTE user would experience in Europe 2016. The end-2-end round trip delay, or responsiveness, of 5G is aimed to be at least 4 times better than the average experienced responsiveness of LTE in 2016. The actual experience gain between LTE and 3G has been between 5 – 10 times in DL speed, approx. 3 –5 times in UL and between 2 to 3 times in latency (i.e., pinging the same server exterior to the mobile network operator).
According with Sandvine’s 2015 report on “Global Internet Phenomena Report for APAC & Europe”, in Europe approx. 46% of the downstream fixedpeak aggregate traffic comes from real-time entertainment services (e.g., video & audio streamed or buffered content such as Netflix, YouTube and IPTV in general). The same report also identifies that for Mobile (in Europe) approx. 36% of the mobile peak aggregate traffic comes from real-time entertainment. It is likely that the real share of real-time entertainment is higher, as video content embedded in social media might not be counted in the category but rather in Social Media. Particular for mobile, this would bring up the share with between 10% to 15% (more in line with what is actually measured inside mobile networks). Real-time entertainment and real-time services in general is the single most important and impacting traffic category for both fixed and mobile networks.
Video viewing experience … more throughput is maybe not better, more could be useless.
Video consumption is a very important component of real-time entertainment. It amounts to more than 90% of the bandwidth consumption in the category. The Table below provides an overview of video formats, number of pixels, and their network throughput requirements. The tabulated screen size is what is required (at a reasonable viewing distance) to detect the benefit of a given video format in comparison with the previous. So in order to really appreciate 4K UHD (ultra high definition) over 1080p FHD (full high definition), you would as a rule of thumb need double the screen size (note there are also other ways to improved the perceived viewing experience). Also for comparison, the Table below includes data for mobile devices, which obviously have a higher screen resolution in terms of pixels per inch (PPI) or dots per inch (DPI). Apart from 4K (~8 MP) and to some extend 8K (~33 MP), the 16K (~132 MP) and 32K (~528 MP) are still very yet exotic standards with limited mass market appeal (at least as of now).
We should keep in mind that there are limits to the human vision with the young and healthy having a substantial better visual acuity than what can be regarded as normal 20/20 vision. Most magazines are printed at 300 DPI and most modern smartphone displays seek to design for 300 DPI (or PPI) or more. Even Steve Jobs has addressed this topic;
However, it is fair to point out that this assumed human vision limitation is debatable (and have been debated a lot). There is little consensus on this, maybe with the exception that the ultimate limit (at a distance of 4 inch or 10 cm) is 876 DPI or approx. 300 DPI (at 11.5 inch / 30 cm).
Anyway, what really matters is the customers experience and what they perceive while using their device (e.g., smartphone, tablet, laptop, TV, etc…).
So lets do the visual acuity math for smartphone like displays;
We see (from the above chart) that for an iPhone 6/7 Plus (5.5” display) any viewing distance above approx. 50 cm, a normal eye (i.e., 20/20 vision) would become insensitive to video formats better than 480p (1 – 2.3 Mbps). In my case, my typical viewing distance is ca. 30+ cm and I might get some benefits from 720p (2.3 – 4.5 Mbps) as opposed to 480p. Sadly my sight is worse than the norm of 20/20 (i.e., old! and let’s just leave it at that!) and thus I remain insensitive to the resolution improvements 720p would provide. If you have a device with at or below 4” display (e.g., iPhone 5 & 4) the viewing distance where normal eyes become insensitive is ca. 30+ cm.
All in all, it would appear that unless cellular user equipment, and the way these are being used, changes very fundamentally the 480p to 720p range might be more than sufficient.
If this is true, it also implies that a cellular 5G user on a reliable good network connection would need no more than 4 – 5 Mbps to get an optimum viewing (and streaming) experience (i.e., 720p resolution).
The 5 Mbps streaming speed, for optimal viewing experience, is very far away from our 5G 1-Gbps promise (x200 times less)!
Assuming instead of streaming we want to download movies, assuming we lots of memory available on our device … hmmm … then a typical 480p movie could be download in ca. 10 – 20 seconds at 1Gbps, a 720p movie between 30 and 40 seconds, and a 1080p would take 40 to 50 seconds (and likely a waste due to limitations to your vision).
However with a 5G promise of super reliable ubiquitous coverage, I really should not need to download and store content locally on storage that might be pretty limited.
Downloads to cellular devices or home storage media appears somewhat archaic. But would benefit from the promised 5G speeds.
I could share my 5G-Gbps with other users in my surrounding. A typical Western European household in 2020 (i.e., about the time when 5G will launch) would have 2.17 inhabitants (2.45 in Central Eastern Europe), watching individual / different real-time content would require multiples of the bandwidth of the optimum video resolution. I could have multiple video streams running in parallel, to likely the many display devices that will be present in the consumer’s home, etc… Still even at fairly high video streaming codecs, a consumer would be far away from consuming the 1-Gbps (imagine if it was 10 Gbps!).
Okay … so video consumption, independent of mobile or fixed devices, does not seem to warrant anywhere near the 1 – 10 Gbps per connection.
Surely EU Commission wants it!
EU Member States have their specific broadband coverage objectives – namely: ‘Universal Broadband Coverage with speeds at least 30 Mbps by 2020’ (i.e, will be met by LTE!) and ‘Broadband Coverage of 50% of households with speeds at least 100 Mbps by 2020 (also likely to be met with LTE and fixed broadband means’.
The European Commission’s “Broadband Coverage in Europe 2015” reports that 49.2% of EU28 Households (HH) have access to 100 Mbps (i.e., 50.8% of all HH have access to less than 100 Mbps) or more and 68.2% to broadband speeds above 30 Mbps (i.e., 32.8% of all HH with access to less than 30 Mbps). No more than 20.9% of HH within EU28 have FTTP (e.g., DE 6.6%, UK UK 1.4%, FR 15.5%, DK 57%).
The EU28 average is pretty good and in line with the target. However, on an individual member state level, there are big differences. Also within each of the EU member states great geographic variation is observed in broadband coverage.
Interesting, the 5G promises to per user connection speed (1 – 10 Gbps), coverage (user perceived 100%) and reliability (user perceived 100%) is far more ambitious that the broadband coverage objectives of the EU member states.
So maybe indeed we could make the EU Commission and Member States happy with the 5G Throughput promise. (this point should not be underestimated).
Web browsing experience … more throughput and all will be okay myth!
So … Surely, the Gbps speeds can help provide a much faster web browsing / surfing experience than what is experienced today for LTE and for the fixed broadband? (if there ever was a real Myth!).
In other words the higher the bandwidth, the better the user’s web surfing experience should become.
While bandwidth (of course) is a factor in customers browsing experience, it is but a factor out of several that also governs the customers real & perceived internet experience; e.g., DNS Lookups (this can really mess up user experience), TCP, SSL/TLS negotiation, HTTP(S) requests, VPN, RTT/Latency, etc…
An excellent account of these various effects is given by Jim Getty’s “Traditional AQM is not enough” (i.e., AQM: Active Queue Management). Measurements (see Jim Getty’s blog) strongly indicates that at a given relative modest bandwidth (>6+ Mbps) there is no longer any noticeable difference in page load time. In my opinion there are a lot of low hanging fruits in network optimization that provides large relative improvements in customer experience than network speed alone..
Thus one might carefully conclude that, above a given throughput threshold it is unlikely that more throughput would have a significant effect on the consumers browsing experience.
More work needs to be done in order to better understand the experience threshold after which more connection bandwidth has diminishing returns on the customer’s browsing experience. However, it would appear that 1-Gbps 5G connection speed would be far above that threshold. An average web page in 2016 was 2.2 MB which from an LTE speed perspective would take 568 ms to load fully provided connection speed was the only limitation (which is not the case). For 5G the same page would download within 18 ms assuming that connection speed was the only limitation.
Downloading content (e.g., FTTP).
Now we surely are talking. If I wanted to download the whole Library of the US Congress (I like digital books!), I am surely in need for speed!?
The US Congress have estimated that the whole print collection (i.e., 26 million books) adds up to 208 terabytes.Thus assuming I have 208+ TB of storage, I could within 20+ (at 1 Gbps) to 2+ (at 20 Gbps) days download the complete library of the US Congress.
In fact, at 1 Gbps would allow me to download 15+ books per second (assuming 1 book is on average 3oo pages and formatted at 600 DPI TIFF which is equivalent to ca. 8 Mega Byte).
So clearly, for massive file sharing (music, videos, games, books, documents, etc…), the 5G speed promise is pretty cool.
Though, it does assume that consumers would continue to see a value in storing information locally on their personally devices or storage medias. The idea remains archaic, but I guess there will always be renaissance folks around.
What about 50 Mbps everywhere (at a 10 ms latency level)?
Firstly, providing a customers with a maximum latency of 10 ms with LTE is extremely challenging. It would be highly unlikely to be achieved within existing LTE networks, particular if transmission retrials are considered. From OpenSignal December 2016 measurements shown in the chart below, for urban areas across Europe, the LTE latency is on average around 41±9 milliseconds. Considering the LTE latency variation we are still 3 – 4 times away from the 5G promise. The country average would be higher than this. Clearly this is one of the reasons why the NGMN whitepaper proposes a new air-interface. As well as some heavy optimization and redesigns in general across our Telco networks.
The urban LTE persistent experience level is very reasonable but remains lower than the 5G promise of 50 Mbps, as can be seen from the chart below;
The LTE challenge however is not the customer experience level in urban areas but on average across a given geography or country. Here LTE performs substantially worse (also on throughput) than what the NGMN whitepaper’s ambition is. Let us have a look at the current LTE experience level in terms of LTE coverage and in terms of (average) speed.
Based on European Commission “Broadband Coverage in Europe 2015” we observe that on average the total LTE household coverage is pretty good on an EU28 level. However, the rural households are in general underserved with LTE. Many of the EU28 countries still lack LTE consistent coverage in rural areas. As lower frequencies (e.g., 700 – 900 MHz) becomes available and can be overlaid on the existing rural networks, often based on 900 MHz grid, LTE rural coverage can be improved greatly. This economically should be synchronized with the normal modernization cycles. However, with the current state of LTE (and rural network deployments) it might be challenging to reach a persistent level of 50 Mbps per connection everywhere. Furthermore, the maximum 10 millisecond latency target is highly unlikely to be feasible with LTE.
In my opinion, 5G would be important in order to uplift the persistent throughput experience to at least 50 Mbps everywhere (including cell edge). A target that would be very challenging to reach with LTE in the network topologies deployed in most countries (i.e., particular outside urban/dense urban areas).
The customer experience value to the general consumer of a maximum 10 millisecond latency is in my opinion difficult to assess. At a 20 ms response time would most experiences appear instantaneous. The LTE performance of ca. 40 ms E2E external server response time, should satisfy most customer experience use case requirements beside maybe VR/AR.
Nevertheless, if the 10 ms 5G latency target can be designed into the 5G standard without negative economical consequences then that might be very fine as well.
Another aspect that should be considered is the additional 5G market potential of providing a persistent 50 Mbps service (at a good enough & low variance latency). Approximately 70% of EU28 households have at least a 30 Mbps broadband speed coverage. If we look at EU28 households with at least 50 Mbps that drops to around 55% household coverage. With the 100% (perceived)coverage & reliability target of 5G as well as 50 Mbps everywhere, one might ponder the 30% to 45% potential of households that are likely underserved in term of reliable good quality broadband. Pending the economics, 5G might be able to deliver good enough service at a substantial lower cost compared more fixed centric means.
Finally, following our expose on video streaming quality, clearly a 50 Mbps persistent 5G connectivity would be more than sufficient to deliver a good viewing experience. Latency would be less of an issue in the viewing experience as longs as the variation in the latency can be kept reasonable low.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
The perception of value is orders of magnitude higher than the willingness to pay, i.e.,
“I would NOT give up Internet for life for a Million+ US Dollars … oh … BUT… I don’t want to pay more than a couple of bucks for it either” (actually for a mature postpaid-rich market the chances are that over your expected life-time you will pay between 30 to 40 thousand US$ for mobile internet & voice & some messaging).
Price plans are fascinating! … Particular the recent data-centric price plans bundling in legacy services such as voice and SMS.
Needles to say that a consumer today often needs an advanced degree in science to really understand the price plans they are being presented. A high degree of trust is involved in choosing a given plan. The consumer usually takes what has been recommended by the shop expert (who most likely doesn’t have an advanced science degree either). This shop expert furthermore might (or might not) get a commission (i.e., a bonus) selling you a particular plan and thus in such a case hardly is the poster child of objectiveness.
How does the pricing experts come to the prices that they offer to the consumer? Are those plans internally consistent … or maybe not?
It becomes particular interesting to study data-centric price plans that try to re-balance Mobile Voice and SMS.
How is 4G (i.e., in Europe also called LTE) being charged versus “normal” data offerings in the market? Do the mobile consumer pay more for Quality? Or maybe less?
What is the real price of mobile data? … Clearly, it is not the price we pay for a data-centric price plan.
A Data-centric Tale of a Country called United & a Telecom Company called Anything Anywhere!
As an example of mobile data pricing and in particular of data-centric mobile pricing with Voice and SMS included, I looked at a Western European Market (let’s call it United) and a mobile operator called Anything Anywhere. Anything Anywhere (AA) is known for its comprehensive & leading-edge 4G network as well as several innovative product ideas around mobile broadband data.
In my chosen Western European country United, voice revenues have rapidly declined over the last 5 years. Between 2009 to 2014 mobile voice revenues lost more than 36% compared to an overall revenue loss of “only” 14%. This corresponds to a compounded annual growth rate of minus 6.3% over the period. For an in depth analysis of the incredible mobile voice revenue losses the mobile industry have incurred in recent years see my blog “The unbearable lightness of mobile voice”.
Did this market experience a massive uptake in prepaid customers? No! Not at all … The prepaid share of the customer base went from ca. 60% in 2009 to ca. 45% in 2014. So in other words the Postpaid base over the period had grown with 15% and in 2014 was around 55%. This should usually have been a cause for great joy and incredible boost in revenues. United is also a market that has largely managed not to capitalize economically on substantial market consolidation.
As it is with many other mobile markets, engaging & embracing the mobile broadband data journey has been followed by a sharp decline in the overall share of voice revenue from ca. 70% in 2009 to ca. 50% in 2014. An ugly trend when the total mobile revenue declines as well.
The Smartphone penetration in United as of Q1 2014 was ca. 71% with 32% iOS-based devices. Compare this to 2009 where the smartphone penetration was ca. 21% with iOS making out around 75+%.
Our Mobile Operator AA has the following price plan structure (note: all information is taken directly from AA’s web site and can be found back if you guess which company it applies to);
Data-centric price plans with unlimited Voice and SMS.
Differentiated speed plans, i.e., 4G (average speed advertised to 12 – 15 Mbps) vs. Double Speed 4G (average speed advertised to 24 – 30 Mbps).
Offer plans that apply Europe Union-wide.
Option to pay less for handsets upfront but more per month (i.e., particular attractive for expensive handsets such as iPhone or Samsung Galaxy top-range models).
Default offering is 24 month although a shorter period is possible as well.
Offer SIM-only data-centric with unlimited voice & SMS.
Offer Data-only SIM-only plans.
Further you will get access to extensive “WiFi Underground”. Are allowed tethering and VoIP including Voice-calling over WiFi.
So here is an example of AA’s data-centric pricing for various data allowances. In this illustration I have chosen to add an iPhone 6 Plus (why? well I do love that phone as it largely replaces my iPad outside my home!) with 128GB storage. This choice have no impact on the fixed and variable parts of the respective price plans. For SIM-Only plans in the data below, I have added the (Apple) retail price of the iPhone 6 Plus (light grey bars). This is to make the comparison somewhat more comparable. It should of course be clear that in the SIM-only plans, the consumer is not obliged to buy a new device.
Figure above: illustrates the total consumer cost or total price paid over the period (in local currency) of different data plans for our leading Western European Mobile Operator AA. The first 9 plans shown above includes a iPhone 6 Plus with 128GB memory. The last 5 are SIM only plans with the last 2 being Data-only SIM-only plans. The abbreviations are the following PPM: Pay per Month (but little upfront for terminal), PUF: Pay UpFront (for terminal) and less per month, SIMO: SIM-Only plan, SIMDO: SIM Data-Only plan, xxGB: The xx amount of Giga Bytes offered in Plan, 2x indicates double 4G speed of “normal” and 1x indicates “normal” speed, 1st UL indicates unlimited voice in plan, 2nd UL indicates unlimited SMS in plan, EU indicates that the plan also applies to countries in EU without extra charges. So PPM20GB2xULULEU defines a Pay per Month plan (i.e., the handset is pay over the contract period and thus leads to higher monthly charges) with 20 GB allowance at Double (4G) Speed with Unlimited Voice and Unlimited SMS valid across EU. In this plan you would pay 100 (in local currency) for a iPhone 6 Plus with 128 GB. Note the local Apple Shop retail price of an iPhone 6 Plus with 128 GB is around 789 in local currency (of which ca. 132 is VAT) for this particular country. Note: for the SIM-only plans (i.e., SIMO & SIMDO) I have added the Apple retail price of a iPhone 6 Plus 128GB. It furthermore should be pointed out that the fixed service fee and the data consumption price does not vary with choice of handset.
If I decide that I really want that iPhone 6 Plus and I do not want to pay the high price (even with discounts) that some price plans offers. AA offers me a 20GB 4G data-plan, pay 100 upfront for the iPhone 6 Plus (with 128 GB memory) and for the next 24 month 63.99 (i.e., as this feels much cheaper than paying 64) per month. After 24 month my total cost of the 20 GB would be 1,636. I could thus save 230 over the 24 month if I wanted to pay 470 (+370 compared to previous plan & – 319 compared to Apple retail price) for the iPhone. In this lower cost plan my monthly cost of the 20 GB would be 38.99 or 25 (40%!) less on a monthly basis.
The Analysis show that a “Pay-less-upfront-and-more-per-month” subscriber would end up after the 24 month having paid at least ca. 761 for the iPhone 6 Plus (with 128GB). We will see later, that the total price paid for the iPhone 6 Plus however is likely to be approximately 792 or slightly above today’s retail price (based on Apple’s pricing).
The Price of a Byte and all that Jazz
So how does the above data-price plans look like in terms of Price-per-Giga-Byte?
Although in most cases not be very clear to the consumer, the data-centric price plan is structured around the price of the primary data allowance (i.e., the variable part) and non-data related bundled services included in the plan (i.e., the fixed service part representing non-data items).
There will be a variable price reflecting the data-centric price-plans data allowance and a “Fixed” Service Fee that capture the price of bundled services such as voice and SMS. Based on total price of the data-centric price plan, it will often appear that the higher the allowance the cheaper does your unit-data “consumption” (or allowance) become. Indicating that volume discounts have been factored into the price-plan. In other words, the higher the data allowance the lower the price per GB allowance.
This is often flawed logic and simply an artefact of the bundled non-data related services being priced into the plan. However, to get to that level of understanding requires a bit of analysis that most of us certainly don’t do before a purchase.
Figure above: Illustrates the unit-price of a Giga Byte (GB) versus AA’s various data-centric price plans. Note the price plans can be decomposed into a variable data-usage attributable price (per GB) and a fixed service fee that accounts for non-data services blended into the price. The Data Consumption per GB is the variable data-usage dependable part of the Price Plan and the Total price per GB is the full price normalized to the plans data consumption allowance.
So with the above we have argued that the total data-centric price can be written as a fixed and a variable part;
As will be described in more detail below, the data-centric price is structured in what can be characterized as a “Fixed Service Fee” and a variable “Data Consumption Price” that depends on a given price-plan’s data allowance (i.e., GB is Giga Byte). The “Data Consumption Price” is variable in nature and while it might be a complex (i.e. in terms of complexity) function of data allowance it typically be of the form with the exponent (i.e., Beta) being 1 or close to 1. In other words the Data Consumptive price is a linear (or approximately so) function of the data allowance. In case is larger than 1, data pricing gets progressively more expensive with increasing allowance (i.e., penalizing high consumption or as I believe right-costing high consumption). For lower than 1, data gets progressively cheaper with increasing data allowances corresponding to volume discounts with the danger of mismatching the data pricing with the cost of delivering the data.
The “Fixed Service Fee” depends on all the non-data related goodies that are added to the data-centric price plan, such as (a) unlimited voice, (b) unlimited SMS, (c) Price plan applies Europe-wide (i.e., EU-Option), (d) handset subsidy recovery fee, (e) maybe a customer management fee, etc..
For most price data-centric plan, If the data-centric price divided by the allowance would be plotted against the allowance in a Log-Log format would result in a fairly straight-line.
Nothing really surprising given the pricing math involved! It is instructive to see what actually happens when we take a data-centric price and divide by the corresponding data allowance;
For very large data allowances the price-centric per GB would asymptotically converge to , i.e., the unit cost of a GB. As is usually a lot smaller than , we see that there is another limit, where the allowance is relative low, where we would see the data-centric pricing per GB slope (in a Log-Log plot) become linear in the data allowance. Typically for allowances from 0.1 GB up towards 50 GB, non-linear slope of approximately -0.7±0.1 is observed and thus in between the linear and the constant pricing regime.
We can also observe that If the total price, of a data-centric price plan associated with a given data allowance (i.e., GB), is used to derive a price-per-GB, one would conclude that most mobile operators provide the consumer with volume discounts as they adapt higher data allowance plans. The GB gets progressively cheaper for higher usage plans. As most data-centric price plans are in the range where is (a lot) smaller than , it will appear that the unit price of data declines as the data allowance increases. However in most cases it is likely an artefact of the Fixed Service Fee that reflects non-data related services which unless a data-only bundle can be a very substantial part of the data-centric price plan.
It is clear that data-allowance normalizing the totality of a data-centric price plan, particular when non-data services have been blended into the plan, will not reveal the real price of data. If used for assessing, for example, data profitability or other mobile data related financial KPIs this approach might be of very little use.
Figure above: illustrates the basic characteristics of a data-centric price plan normalized by the data allowance. The data for this example reflects the AA’s data-centric price plans 2x4G Speed with bundled unlimited Voice & SMS as well as applying EU-wide. We see that the Beta value corresponds to a Volume Discount (at values lower than 1) or a Volume Penalty (at values higher than 1).
Oh yeah! … The really “funny” part of most data-price plan analysis (including my own past ones!) are they are more likely to reflect the Fixed Service Part (independent of the Data allowance) of the Data-centric price plan than the actual unit price of mobile data.
What to expect from AA’s data-centric price plans?
so in a rational world of data-centric pricing (assuming such exist) what should we expect of Anything Anywhere’s price plans as advertised online;
The (embedded) price for unlimited voice would be the same irrespective of the data plan’s allowed data usage (i.e., unlimited Voice does not depend on data plan).
The (embedded) price for unlimited SMS would be the same irrespective of the data plan’s allowed data usage (i.e., unlimited SMS does not depend on data plan).
You would pay more for having your plan extended to apply across Europe Union compared to not having this option.
You would (actually you should) expect to pay more per Mega Byte for the Double Speed option as compared to the Single Speed Option.
If you decide to “finance” your handset purchase (i.e., pay less upfront option) within a data plan you should expect to pay more on a monthly basis.
Given a data plan has a whole range of associated handsets priced From Free (i.e., included in plan without extra upfront charge) to high-end high-priced Smartphones, such as iPhone 6 Plus 128 GB, you would not expect that handset related cost would have been priced into the data plan. Or if it is, it must be the lowest common denominator for the whole range of offered handsets at a given price plan.
Where the discussion becomes really interesting is how your data consumption should be priced; (1) You pay more per unit of data consumption as you consume more data on a monthly basis, (2) You pay the same per unit irrespective of your consumption or (3) You should have a volume discount making your units cheaper the more you consume.
of course the above is if and only if the price plans have been developed in reasonable self-consistent manner.
Figure above: Illustrates AA’s various data-centric price plans (taken from their web site). Note that PPM represents low upfront (terminal) cost for the consumer and higher monthly cost and PUF represent paying upfront for the handset and thus having lower monthly costs as a consequence. The Operator AA allows the consumer in the PPM Plan to choose for an iPhone 6 Plus 128GB (priced at 100 to 160) or an IPhone 6 Plus 64GB option (at a lower price of course).
First note that Price Plans (with more than 2 data points) tend to be linear with the Data Usage allowance.
The Fixed Service Fee – The Art of Re-Capture Lost legacy Value?
In the following I define the Fixed Service Fee as the part of the total data-centric price plan that is independent of a given plan’s data allowance. The logic is that this part would contain all non-data related cost such as Unlimited Voice, Unlimited SMS, EU-Option, etc..
From AA’s voice plan (for 250 Minutes @ 10 per Month & 750 Minutes @ 15 per Month) with unlimited SMS (& no data) it can be inferred that
Price of Unlimited SMS can be no higher than 7.5. This however is likely also include general customer maintenance cost.
Monthly customer maintenance cost (cost of billing, storage, customer care & systems support, etc.) might be deduced from the SIM-Only Data-Only package and would be
Price of Monthly Customer Maintenance could be in the order of 5, which would imply that the Unlimited SMS price would be 2.5. Note the market average Postpaid SMS ARPU in 2014 was ca., 8.40 (based on Pyramid Research data). The market average number of postpaid SMS per month was ca. 273 SMS.
From AA’s SIM-only plan we get that the fixed portion of providing service (i.e., customer maintenance, unlimited Voice & SMS usage) is 14 and thus
Price of Unlimited Voice should be approximately 6.5. Note the market average Postpaid Voice ARPU was ca. 12 (based on Pyramid Research data). The market average voice usage per month was ca. 337 minutes. Further from the available limited voice price plans it can be deduced that unlimited voice must be higher than 1,000 Minutes or more than 3 times the national postpaid average.
The fixed part of the data-centric pricing difference between the data-centric SIM-only plan and similar data-centric plan including a handset (i.e., all services are the same except for the addition of the handset) could be regarded as a minimum handset financing cost allowing the operator to recover some of the handset subsidy
Equipment subsidy recovery cost of 7 (i.e., over a 24 month period this amounts to 168 which is likely to recover the average handset subsidy). Note is the customer chooses to pay little upfront for the handset, the customer would have to pay 26 extra per month in he fixed service fee. Thus low upfront cost result in another 624 over the 24 month contract period. Interestingly is that with the initial 7 for handset subsidy recovery in the basic fixed service fee a customer would have paid 792 in handset recovery over 24 month period the contract applies to (a bit more than the iPhone 6 Plus 128GB retail price).
The price for allowing the data-centric price-plan to apply Europe Union Wide is
The EU-Option (i.e., plan applicable within EU) appears to be priced at ca. 5 (caution: 2x4G vis-a-vis 1x4G could have been priced into this delta as well).
For EU-option price it should be noted here that the two plans that are being compared differs not only in the EU-option. The plan without the EU option is a data plan with “normal” 4G speed, while the EU-option plan supports double 4G speeds. So in theory the additional EU-option charge of 5 could also include a surcharge for the additional speed.
Why an operator would add the double speed to the fixed Service Fee price part is “bit” strange. The 2x4G speed price-plan option clearly is a variable trigger for cost (and value to the customer’s data usage). Thus should be introduced in the the variable part (i.e., the Giga-Byte dependent part) of the data-centric price plan.
It is assumed that indeed the derived difference can be attributed to the EU-option, i.e., the double speed has not been include in the monthly Fixed Service Fee.
In summary we get AA’s data-centric price plan’s monthly Fixed Service Fee de-composition as follows;
Figure above: shows the composition of the monthly fixed service fee as part of AA’s data-centric plans. Of course in a SIM-only scenario the consumer would not have the Handset Recovery Fee inserted in the price plan.
So irrespective of the data allowance a (postpaid) customer would pay between 26 to 52 per month depending on whether handset financing is chosen (i.e., Low upfront payment on the expense of higher monthly cost).
Mobile data usage still has to happen!
The price of Mobile Data Allowance.
The variable data-price in the studied date-centric price plans are summarized in the table below as well as the figure;
Price per GB
Pay Less Upfront More per Month
Pay Upfront & Less per Month
SIM-Only Data Only
2 (only 2 data points)
The first thing that obviously should make you Stop in Wonder is that Single 4G Speed Giga Byte is more than Twice the price of a Double 4G Speed Giga Byte … In need for speed … well that will give you a pretty good deal with AA’s price 2x4G plans.
Second thing to notice is that it would appear to be a really bad deal (with respect to the price-per-byte) to be a SIM-Only Data-Only customer.
The Data-Only pays 2 per GB. Almost 3 times more than if you would choose a subscription with a device, double speed, double unlimited and EU-wide applicable price plan.
Agreed! In absolute terms the SIM-only Data-only cost a lot less per month (9 less than the 20GB pay device upfront) and it is possible to run away after 12 months (versus the 24 month plans). One rationale for charging extra per Byte for a SIM-only Data-only plan could be that the SIM card might be used in Tablet or Data-card/Dongle products that typically does consume most if not all of a given plans allowance. For normal devices and high allowance plans on average the consumption can be quiet a lot lower than the actual allowance. Particular over a 24 month period.
You might argue that this is all about how the data-centric price plans have been de-composed in a fixed service fee (supposedly the non-data dependent component) and a data consumptive price. However, even when considering the full price of a given price plan is the Single-4G-Speed more expensive per Byte than Double-4G-Speed.
You may also argue that I am comparing apples and oranges (or even bananas pending taste) as the Double-4G-Speed plans include a devices and a price-plan that applies EU-wide versus the SIM-only plan that includes the customers own device and a price-plan that only works in United. All true of course … Why that should be more expensive to opt out of is a bit beyond me and why this should have an inflationary impact on the price-per-Byte … well a bit of a mystery as well.
At least there is no (statistical) difference in the variable price of a Giga Byte whether the customer chooses to pay of her device over the 24 month contract period or pay (most of) it upfront.
For AA it doesn’t seem to be of concern! …. As 88% would come back for more (according with their web site).
Obviously this whole analysis above make the big assumption that the data-centric price plans are somewhat rationally derived … this might not be the case!
and it assumes that rationally & transparently derived price plans are the best for the consumer …
and it assumes what is good for the consumer is also good for the company …
Is AA different in this respect to that of other Operators around the world …
No! AA is not different from any other incumbent operator coming from a mobile voice centric domain!
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
Postscript – The way I like to look at (rational … what ever that means) data-centric pricing.
Firstly, it would appear that AA’s pricing philosophy follows the industry standard of pricing mobile services and in particular mobile data-centric services by the data volume allowance. Non-data services are added to the data-centric price plan and in all effect make up for the most part of the price-plan even at relative higher data allowances;
Figure above: illustrates the typical approach to price plan design in the Telecom’s industry. Note while not per se wrong it often overweight’s the volume element of pricing and often results in sub-optimizing the Quality and Product aspects . Source: Dr. Kim K Larsen’s Mind Share contribution at Informa’s LTE World Summit May 2012; “Right pricing LTE and mobile broadband in general (a Technologist’ Observations)”.
Unlimited Voice and SMS in AA’s standard data-centric plans clearly should mitigate possible loss or migration away from old fashion voice (i.e., circuit switched) and SMS. However both the estimated allowances for unlimited voice (6.5) and SMS (2.5) appear to be a lot lower than their classical standalone ARPUs for the postpaid category. This certainly could explain that this market (as many others in Western Europe) have lost massive amount of voice revenues over the last 5 years. In other words re-capturing or re-balancing legacy service revenues into data-centric plans still have some way to go in order to be truly effective (if at all possible which is highly questionable at this time and age).
As a Technologist, I am particular interested in how the technology cost and benefits are being considered in data-centric price plans.
The big challenge for the pricing expert who focus too much on volume is that the same volume can result from vastly different network qualities and speed. The customers handset will drive the experience of quality and certainly consumption. By that differences in network load and thus technology cost. A customer with a iPhone 6 Plus is likely to load the mobile data network more (and thus incur higher cost) than a customer with a normal screen smartphone of 1 or 2 generations removed from iPhone 6 Plus. It is even conceivable that a user with iPhone 6 Plus will load the network more than a customer with a normal iPhone 6 (independent of the iOS). This is very very different for Voice and SMS volumetric considerations in legacy price plans, where handset had little (or no) impact on network load relative to the usage.
For data-centric price plans to be consistent with the technology cost incurred one should consider;
Higher “guarantied” Quality, typically speed or latency, should be priced higher per Byte than lower quality plans (or at the very least not lower).
Higher Volumetric Allowances should be priced per Byte higher than Lower Volumetric Allowance (or at the very least not lower).
Offering unlimited Voice & SMS in data-centric plans (as well as other bundled goodies) should be carefully re-balanced to re-capture some of lost legacy revenues.
That AA’s data-centric plans for double speed appears to be cheaper than their plans at a lower data delivery quality level is not consistent with costing. Of course, AA cannot really guaranty that the customer will get double 4G speed everywhere and as such it may not be fair to charge substantially more than for single speed. However, this is of course not what appear to happen here.
AA’s lowest data unit price (in per Giga Byte) is around 0.6 – 0.7 (or 0.06 – 0.07 Cent per Mega Byte). That price is very low and in all likelihood lower than their actual production cost of a GB or MB.
However, one may argue that as long as the Total Service Revenue gained by a data-centric price plan recover the production cost, as well as providing a healthy margin then whether the applied data unit-price is designed to recover the data production cost is maybe less of an issue.
In other words, data profitability may not matter as much as overall profitability. This said it remains in my opinion in-excusable for a mobile operator not to understand its main (data) cost drivers and ensure it is recovered in their overall pricing strategies.
Surely! You may say? … “Surely Mobile Operators know their cost structure and respective cost drivers and their price plans reflects this knowledge?”
It is my observation that most price plans (data-centric or not) are developed primarily in response to competition (which of course is an important pricing element as well) rather than firmly anchored in Cost, Value & Profit considerations. Do Operators really & deeply know their own cost structure and cost drivers? … Ahhh … In my opinion few really appear to do!
1000 Days & a bit … As of January 2015 a Facebook share was around Double the price of their initial public offering (IPO) back in May 2012.
That increase in share price & value correspond to an investor belief (conscious or otherwise) that Facebook’s can grab around 40% of the online spend in the (near) Future versus a mediocre 20+% back in 2012.
If you had bought Facebook shares on September 4th 2012 and sold them again December 22nd 2014 (yes just before Christmas) you would have earned 5 times your original investment!
Back in 2012, some month after Facebook’s (FB) IPO, I wrote a Blog Facebook Values … Has the little boy spoken? on the FB value and how to get a feel whether the share price was Naked (“The Ugly”), Half Dressed (“The Bad”) or Nicely Dressed (“The Good”).
I asked whether it was a good time to have confidence and invest in Facebook … My answer (at the time) was that the share price of about 20 US$ (August 2012 timeframe) appeared too low compared with the potential for capturing Online Advertisement Spending Market Share, that furthermore was poised to increase substantially going forward as funds were being redirected from traditional advertisement spend to digital / online media.
The primary source of revenue for Facebook (then & now) is from Online Advertisement Spend. Thus, I looked at what long-term share (chosen arbitrarily to be 5 years) of the Online Ad Market should Facebook have in order to justify its value and share price. Very simply I ramped up the revenue share from its current value to a target share that would correspond to Facebook’s Market Capitalization or Share Price.
In the following I will ignore all the goodies that Facebook have launched or acquired over their lifetime, such as
All of the above (and much more) serves to make sure that “People use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them”. (i.e., Facebook’s vision statement) … and (I assume) to make maximum profit out of the Facebook “addiction” by providing a very efficient advertisement platform enabled by the gazillion of personal data/information & impressions we all continuously volunteer by using Facebook.
However, while the technologies (e.g., algorithms & communications software utilities) behind are very exiting it all serves one purpose … deliver the most efficient ad to the user and make as much money out of that customer touch point. I believe that the potential and value of Whatsapp is huge and in message volume already exceeds the number daily SMS transactions globally. This still largely remains un-explored by Facebook. The question will be whether FB will primarily use Whatsapp as another Ad delivery vehicle or also as a mean to generate communications revenues in both the messaging and the voice consumer segments.
The conclusion back in August 2012, was that the share price of Facebook, based on its equivalent long-term share of the the Online Ad Market Spend, appeared low and one should expect the price (and value) to increase.
Above figure: Analysis presented in my 2012 “Facebook – time to invest?”. Note that the the share price dynamics are illustrated relative to the IPO price of 38 US$ or more accurately the stock price at closing on 18th of May 2012.
So what has happened in the almost 1000 days since the Facebook IPO?
Well after the share price dropped to 17.73 (at closing) on September 4th (2012), which roughly halved the Market Cap of Facebook, the FB Journey has been one of growth.
The cynic might of course point out that so has the rest of the market. However, while for example Nasdaq100 is ca. 68% higher (as of January 20th 2015) compared to the 18-May 2012, Facebook is almost double its IPO value (over the same period). If you take the lowest point (4 Sep 2012) and the highest point (22 Dec 2014) you have a 4.6 times ratio between high & low points.
So if you did the right thing and bought the lowest and sold at the highest … well I told you so (joking!) … Congratulation would be called for!
Here is the FB Journey ( = A Walk on the Wilder Side?) seen from an investors perspective
Above figure: illustrates Facebook stock price development since the IPO until end of January 2015 with commentary to the peaks and the dips.The Red Dot on September 4th 2012 represents the lowest historical share price (i.e., 17.71 @ closing) and the Green Dot the highest historical share price (i.e., 81.45 @ closing).
Taking the stock price dynamics as shown above, how would the previous analysis come out looking at what Online Ad Revenue Share could justify (approximately) the share price development over the period.
Well … it could look something like this based on Online Advertisement Market Share;
Above Figure: illustrates the share price dynamics relative to the IPO price, i.e., 100% level (at closing 18 May 2012). Further a relative simple valuation model based on Facebook’s long-term (i.e., 5+ years) online advertisement market share is used to derive the Online Ad Spend share that a given FB share price corresponds to. The methodology has been described in detail in “A walk on the Wild Side”.
Current stock price range is fairly consistent with a 40+% long-term (i.e., 5 year linear ramp up from 2013 share and then keeping the share at the level going forward) share of the Online Advertisement Market. I would expect Facebook to hit at least 10% for 2014 (based on eMarketer data of the total online advertisement market).
Will Facebook be able to grow to 40+% share of the Online Ad Spending?
In my opinion that does not sound completely un-reasonable …
Though it would imply that (a) other social media players also relying on the Online Ad Market are going to lose their livelihood, some (b)business plans might look somewhat more sombre for others and (c)Facebook needs to take Google Head-On.
Facebook beating Q4 earnings expectations and came out at 3.851B US$ coming out at 3.84B US$ (Low) to 4.16B (US$) (high).
MAU 1,393 1,386 Million and Mobile MAU of 1,189 1,182 Million (both lower limit estimates).
Share price at closing was slightly higher than previous trading day ending at 76.24 US$ per share (+0.6%) will drop during the day of the earning call to around 71 – 72 US$ per share.
Given the already high value of the stock, I do not expect much gain over the days after the earning call. Likely to recover to the level 5 – 10 days before the Q4/Full 2014 Earning Call.
… and I might be completely mistaken (and will be crying all the way to the bank)… but at least we will know within the next hours & days to come!
… So I ended up being fairly close to MAU (0.5% lower), Mobile MAU (0.6% lower) and earnings expectations (0.3% from my lower bound). However, predicting the stock movement … yeah … not so good. Still next couple of days will be interesting to follow. Dave Wehner, Facebook Chief Financial Officer, was where careful in managing expectations for Facebook Topline in 2015. Concerns about exchange rate effects on the Topline could result in a 5% lower revenue than it would have been with 2014 exchange rates. Basically the revenue growth in US$ would reduce somewhat due to exchange rate effects. This is likely to have some negative impact on Facebook profitability and their margin as their fundamental cost base is in US$.
FB gains a sustainable share of online ad spend X%.
5 yr linear ramp-up from 2014 9.6% (assessed) to X%, and then maintained at that level.
Other revenues 15% in 2014, linearly reduced to 10% after 5 yrs and then maintained.
Assume FB can maintain a free cash flow yield of 25%.
It should be noted that the above analysis is in all likelihood oversimplifying. However it is not terrible difficult to add complexity. Though given the inherent uncertainties involved in predicting the future, the approach presented is good enough to get an idea about a given investments (or stock purchase) attractiveness.
For the financial history buffs, the Nasdaq100 is ca. 5% from the level of the dot.com foreshock (or pre-crash) of March-2000 and has surpassed the big crash of July-2000.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
Figure above: shows the share price development from May 18 2012 (IPO date of FB) to 20 January 2015 of Facebook and Nasdaq100 composite. While Facebook largely under-performed in 2012 and well into 2013, its recovery from mid-2013 until January 2015 has been spectacular.
Figure above: illustrates Laplace distribution representations of the daily returns of Facebook and Nasdaq100 over the period from 18 May 2012 to 20 January 2015. Note the slight right shift in centre point from the 0%. For a more detailed analysis of the Nasdaq100 and application of the Laplace distribution see the Business Forecasting Blog “The Nasdaq100 Daily Returns and Laplace Distributed Errors”.
Figure above: Online Advertisement Spending forecast from eMarketer (August 2013) representing the period 2013 to 2017. From 2018 and to 2022 Forecasts have been extrapolated based on 1st and 2nd derivative of the previous period growth. The resulting trend have been checked against other available projections.
Figure above: illustrates for 2013 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (source: Facebook Annual Report 2013), Region’s share of Ad Spend (source: eMarketer), Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, source: Pyramid Research), and (fixed) internet penetration (i.e., the percentage of population having access to internet).
Figure above: illustrates for 2017 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source: Authors Facebook Model), Region’s share of Ad Spend (Source: eMarketer), Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research), and (fixed) internet penetration (i.e., the percentage of population having access to internet).
Figure above: illustrates for 2013 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source: Facebook Annual Report 2013), LTE penetration (Source: Pyramid Research), WiFi residential potential estimated from the broadband household penetration (Source: Pyramid Research), and Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research). For Facebook’s Autoplay Video feature it is important for the user to either have WiFi access or for a decent cellular performance LTE.
Figure above: illustrates for 2017 Facebook Monthly Active Users (MAU) in terms of share of population versus Region (Source:Source: Authors Facebook Model), LTE penetration (Source: Pyramid Research), WiFi residential potential estimated from the broadband household penetration (Source: Pyramid Research), and Mobile Internet Penetration (i.e., CDMA2000, UMTS, HSPA, LTE, Mobile WiMax, Source: Pyramid Research). For Facebook’s Autoplay Video feature it is important for the user to either have WiFi access or for a decent cellular performance LTE.
A Mature & Emerging Market Profitability Analysis … From Past, through Present & to the Future.
I dedicate this Blog to David Haszeldine whom has been (and will remain) a true partner when it comes to discussing, thinking and challenging cost structures, corporate excesses and optimizing the Telco profitability.
Opex growth & declining revenue growth is the biggest exposure to margin decline & profitability risk for emerging growth markets as well as mature mobile markets.
48 Major Mobile Market’s Revenue & Opex Growth have been analyzed over the period 2007 to 2013 (for some countries from 2003 to 2013). The results have been provided in an easy to compare overview chart.
For 23 out of the 48 Mobile Markets, Opex have grown faster than Revenue and poses a substantial risk to Telco profitability in the near & long-term unless Opex will be better managed and controlled.
Mobile Profitability Risk is a substantial Emerging Growth Market Problem where cost has grown much faster than the corresponding Revenues.
11 Major Emerging Growth Markets have had an Opex compounded annual growth rate between 2007 to 2013 that was higher than the Revenue Growth substantially squeezing margin and straining EBITDA.
On average the compounded annual growth rate of Opex grew 2.2% faster than corresponding Revenue over the period 2007 to 2013. Between 2012 to 2013 Opex grew (on average) 3.7% faster than Revenue.
A Market Profit Sustainability Risk Index (based on Bayesian inference) is proposed as a way to provide an overview of mobile markets profitability directions based on their Revenue and Opex growth rates.
Statistical Analysis on available data shows that a Mobile Markets Opex level is driven by (1) Population, (2) Customers, (3) Penetration and (4) ARPU. The GDP & Surface Area have only minor and indirect influence on the various markets Opex levels.
A profitability framework for understanding individual operators profit dynamics is proposed.
It is shown that Profitability can be written as withbeing the margin, with ou and ru being the user dependent OpEx and Revenue (i.e., AOPU and ARPU), of the fixed OpEx divided by the Total Subscriber Market andis the subscriber market share.
The proposed operator profitability framework provides a high degree of descriptive power and understanding of individual operators margin dynamics as a function of subscriber market share as well as other important economical drivers.
I have long & frequently been pondering over the mobile industry’s profitability.In particular, I have spend a lot of my time researching the structure & dynamics of profitability and mapping out factors that contributes in both negative & positive ways? My interest is the underlying cost structures and business models that drives the profitability in both good and bad ways. I have met Executives who felt a similar passion for strategizing, optimizing and managing their companies Telco cost structures and thereby profit and I have also met Executives who mainly cared for the Revenue.
Obviously, both Revenue and Cost are important to optimize. This said it is wise to keep in mind the following Cost- structure & Revenue Heuristics;
Cost is an almost Certainty once made & Revenues are by nature Uncertain.
Cost left Unmanaged will by default Increase over time.
Revenue is more likely to Decrease over time than increase.
Majority of Cost exist on a different & longer time-scale than Revenue.
In the following I will use EBITDA, which stands for Earnings Before Interest, Taxes, Depreciation and Amortization, as a measure of profitability and EBITDA to Revenue Ratio as a measure of my profit margin or just margin. It should be clear that EBITDA is a proxy of profitability and as such have shortfalls in specific Accounting and P&L Scenarios. Also according with GAAP (General Accepted Accounting Principles) and under IFRS (International Financial Reporting Standards) EBITDA is not a standardized accepted accounting measure. Nevertheless, both EBITDA and EBITDA Margin are widely accepted and used in the mobile industry as a proxy for operational performance and profitability. I am going to assume that for most purposes & examples discussed in this Blog, EBITDA & the corresponding Margin remains sufficiently good measures profitability.
While I am touching upon mobile revenues as an issue for profitability, I am not going to provide much thoughts on how to boost revenues or add new incremental revenues that might compensate from loss of mobile legacy service revenues (i.e., voice, messaging and access). My revenue focus in particular addresses revenue growth on a more generalized level compared to the mobile cost being incurred operating such services in particular and a mobile business in general. For an in-depth and beautiful treatment of mobile revenues past, present and future, I would like to refer to Chetan Sharma’s 2012 paper “Operator’s Dilemma (and Opportunity): The 4th Wave” (note: you can download the paper by following the link in the html article) on mobile revenue dynamics from (1) Voice (1st Revenue or Service Wave), (2) Messaging (2nd Revenue or Service Wave) to todays (3) Access (3rd Revenue Wave) and the commence to what Chetan Sharma defines as the 4th Wave of Revenues (note: think of waves as S-curves describing initial growth spurt, slow down phase, stagnation and eventually decline) which really describes a collection of revenue or service waves (i.e., S-curves) representing a portfolio of Digital Services, such as (a) Connected Home, (b) Connected Car, (c) Health, (d) Payment, (e) Commerce, (f) Advertising, (g) Cloud Services (h) Enterprise solutions, (i) Identity, Profile & Analysis etc.. I feel confident that adding any Digital Service enabled by Internet-of-Things (IoT) and M2M would be important inclusions to the Digital Services Wave. Given the competition (i.e., Facebook, Google, Amazon, Ebay, etc..) that mobile operators will face entering the 4th Wave of Digital Services Space, in combination with having only national or limited international scale, will make this area a tough challenge to return direct profit on. The inherent limited international or national-only scale appears to be one of the biggest barrier to turn many of the proposed Digital Services, particular with those with strong Social Media Touch Points, into meaningful business opportunities for mobile operators.
This said, I do believe (strongly) that Telecom Operators have very good opportunities for winning Digital Services Battles in areas where their physical infrastructure (including Spectrum & IT Architecture) is an asset and essential for delivering secure, private and reliable services. Local regulation and privacy laws may indeed turn out to be a blessing for Telecom Operators and other national-oriented businesses. The current privacy trend and general consumer suspicion of American-based Global Digital Services / Social Media Enterprises may create new revenue opportunities for national-focused mobile operators as well as for other national-oriented digital businesses. In particular if Telco Operators work together creating Digital Services working across operator’s networks, platforms and beyond (e.g., payment, health, private search, …) rather than walled-garden digital services, they might become very credible alternatives to multi-national offerings. It is highly likely that consumers would be more willing to trust national mobile operator entities with her or his personal data & money (in fact they already do that in many areas) than a multinational social-media corporation. In addition to the above Digital Services, I do expect that Mobile/Telecom Operators and Entertainment Networks (e.g., satellite, cable, IP-based) will increasingly firm up partnerships as well as acquire & merge their businesses & business models. In all effect this is already happening.
For emerging growth markets without extensive and reliable fixed broadband infrastructures, high-quality (& likely higher cost compared to today’s networks!) mobile broadband infrastructures would be essential to drive additional Digital Services and respective revenues as well as for new entertainment business models (other than existing Satellite TV). Anyway, Chetan captures these Digital Services (or 4th Wave) revenue streams very nicely and I recommend very much to read his articles in general (i.e., including “Mobile 4th Wave: The Evolution of the Next Trillion Dollars” which is the 2nd “4th Wave” article).
Back to mobile profitability and how to ensure that the mobile business model doesn’t breakdown as revenue growth starts to slow down and decline while the growth of mobile cost overtakes the revenue growth.
A good friend of mine, who also is a great and successful CFO, stated that “Profitability is rarely a problem to achieve (in the short term)”;“I turn down my market invest (i.e., OpEx) and my Profitability (as measured in terms of EBITDA) goes up. All I have done is getting my business profitable in the short term without having created any sustainable value or profit by this. Just engineered my bonus.”
Our aim must be to ensure sustainable and stable profitability. This can only be done by understanding, careful managing and engineering our basic Telco cost structures.
While most Telco’s tend to plan several years ahead for Capital Expenditures (CapEx) and often with a high degree of sophistication, the same Telco’s mainly focus on one (1!) year ahead for OpEx. Further effort channeled into OpEx is frequently highly simplistic and at times in-consistent with the planned CapEx. Obviously, in the growth phase of the business cycle one may take the easy way out on OpEx and focus more on the required CapEx to grow the business. However, as the time-scales for committed OpEx “lives” on a much longer period than Revenue (particular Prepaid Revenue or even CapEx for that matter), any shortfall in Revenue and Profitability will be much more difficult to mitigate by OpEx measures that takes time to become effective. In markets with little or no market investment the penalty can be even harsher as there is no or little OpEx cushion that can be used to soften a disappointing direction in profitability.
How come a telecom business in Asia, or other emerging growth markets around the world, can maintain, by European standards, such incredible high EBITDA Margins. Margin’s that run into 50s or even higher. Is this “just” a matter of different lower-cost & low GDP economies? Does the higher margins simply reflect a different stage in the business cycle (i.e., growth versus super-saturation)?, Should Mature Market really care too much about Emerging Growth Markets? in the sense of whether Mature Markets can learn anything from Emerging Growth Markets and maybe even vice versa? (i.e., certainly mature markets have made many mistakes, particular when shifting gears from growth to what should be sustainability).
Before all those questions have much of a meaning, it might be instructive to look at the differences between a Mature Market and an Emerging Growth Market. I obviously would not have started this Blog, unless I believe that there are important lessons to be had by understanding what is going on in both types of markets. I also should make it clear that I am only using the term Emerging Growth Markets as most of the markets I study is typically defined as such by economists and consultants. However from a mobile technology perspective few of those markets we tend to call Emerging Growth Markets can really be called emerging any longer and growth has slowed down a lot in most of those markets. This said, from a mobile broadband perspective most of the markets defined in this analysis as Emerging Growth Markets are pretty much dead on that definition.
Whether the emerging markets really should be looking forward to mobile broadband data growth might depend a lot on whether you are the consumer or the provider of services.
For most Mature Markets the introduction of 3G and mobile broadband data heralded a massive slow-down and in some cases even decline in revenue. This imposed severe strains on Mobile Margins and their EBITDAs. Today most mature markets mobile operators are facing a negative revenue growth rate and is “forced” continuously keep a razor focus on OpEx, Mitigating the revenue decline keeping Margin and EBITDA reasonably in check.
Emerging Markets should as early as possible focus on their operational expenses and Optimize with a Vengeance.
Well well let ‘s get back to the comparison and see what we can learn!
It doesn’t take to long to make a list of some of the key and maybe at times obvious differentiators (not intended to be exhaustive) between Mature and Emerging Markets are;
Side Note: it should be clear that by today many of the markets we used to call emerging growth markets are from mobile telephony penetration & business development certainly not emerging any longer and as growing as they were 5 or 10 years ago. This said from a 3G/4G mobile broadband data penetration perspective it might still be fair to characterize those markets as emerging and growing. Though as mature markets have seen that journey is not per se a financial growth story.
Looking at the above table we can assess that Firstly: the straightforward (and possible naïve) explanation of relative profitability differences between Mature and Emerging Markets, might be that emerging markets cost structures are much more favorable compared to what we find in mature market economies. Basically the difference between Low and High GDP economies. However, we should not allow ourselves to be too naïve here as lessons learned from low GDP economies are that some cost structure elements (e.g., real estate, fuel, electricity, etc..) are as costly (some times more so) than what we find back in mature high/higher GDP markets. Secondly: many emerging growth market’s economies are substantially more populous & dense than what we find in mature markets (i.e., although it is hard to beat Netherlands or the Ruhr Area in Germany). Maybe the higher population count & population density leads to better scale than can be achieved in mature markets. However, while maybe true for the urban population, emerging markets tend to have substantially higher ratio of their population living in rural areas compared to what we find in mature markets. Thirdly: maybe the go-to-market approach in emerging markets is different from mature markets (e.g., subsidies, quality including network coverage, marketing,…) offering substantially lower mobile quality overall compared to what is the practice in mature markets. Providing poor mobile network quality certainly have been a recurring theme in the Philippines mobile industry despite the Telco Industry in Philippines enjoys Margins that most mature markets operators can only dream of. It is pretty clear that for 3G-UMTS based mobile broadband, 900 MHz does not have sufficient bandwidth to support the anticipated mobile broadband uptake in emerging markets (e.g., particular as 900MHz is occupied by 2G-GSM as well). IF emerging markets mobile operators will want to offer mobile data at reasonable quality levels (i.e., and the IF is intentional), sustain anticipated customer demand and growth they are likely to require network densification (i.e., extra CapEx and OpEx) at 2100 MHz. Alternative they might choose to wait for APT 700 MHz and drive an affordable low-cost LTE device ecosystem albeit this is some years ahead.
More than likely some of the answers of why emerging markets have a much better margins (at the moment at least) will have to do with cost-structure differences combined with possibly better scale and different go-to-market requirements more than compensating the low revenue per user.
Let us have a look at the usual suspects towards the differences between mature & emerging markets. The EBITDA can be derived as Revenue minus the Operational Expenses (i.e., OpEx) and the corresponding margin is Ebitda divided by the Revenue (ignoring special accounting effects that here);
The EBITDA & Margin tells us in absolute and relative terms how much of our Revenue we keep after all our Operational expenses (i.e., OpEx) have been paid (i.e., beside tax, interests, depreciation & amortization charges).
We can write Revenue as a the product of ARPU (Average Number of Users) times Number of Users N and thus the EBITDA can also be written as;
. We see that even if ARPU is low (or very) low, an Emerging Market with lot of users might match the Revenue of a Mature Market with higher ARPU and worse population scale (i.e., lower amount of users). Pretty simple!
But what about the Margin? , in order for an Emerging Market to have substantially better Margin than corresponding Mature Market at the same revenue level, it is clear that the Emerging Market’s OpEx (O) needs to be lower than that of a Mature markets. We also observe that if the Emerging Market Revenue is lower than the Mature Market, the corresponding Opex needs to be even lower than if the Revenues were identical. One would expect that lower GDP countries would have lower Opex (or Cost in general) combined with better population scale is really what makes for a great emerging market mobile Margins! … Or is it ?
A Small but essential de-tour into Cost Structure.
Some of the answers towards the differences in margin between mature and emerging markets obviously lay in the OpEx part or in the Cost-structure differences. Let’s take a look at a mature market’s cost structure (i.e., as you will find in Western & Eastern Europe) which pretty much looks like this;
With the following OpEx or cost-structure elements;
Usage-related OpEx:typically take up between 10% to 35% of of the total OpEx with an average of ca. 25%. On average this OpEx contribution is approximately 17% of the revenue in mature European markets. Trend wise it is declining. Usage-based OpEx is dominated by interconnect & roaming voice traffic and to a less degree of data interconnect and peering. In a scenario where there is little circuit switched voice left (i.e., the ultimate LTE scenario) this cost element will diminish substantially from the operators cost structure. It should be noted that this also to some extend is being influenced by regulatory forces.
Market Invest: can be decomposed into Subscriber Acquisition Cost (SAC), i.e., “bribing” the customers to leave your competitor for yourself, Subscriber Retention Cost (SRC), i.e., “bribing” your existing (valuable) customers to not let them be “bribed” by you’re a competitor and leave you (i.e., churn), and lastly Other Marketing spend for advertisement, promotional and so forth. This cost-structure element contribution to OpEx can vary greatly depending on the market composition. In Europe’s mature markets it will vary from 10% to 31% with a mean value of ca. 23% of the total OpEx. On average it will be around 14% of the Revenue. It should be noted that as the mobile penetration increases and enter into heavy saturation (i.e., >100%), SAC tends to reduce and SRC will increase. Further in markets that are very prepaid heavy SAC and SRC will naturally be fairly minor cost structure element (i.e., 10% of OpEx or lower and only a couple of % of Revenue). Profit and Margin can rapidly be influenced by changes in the market invest. SAC and SRC cost-structure elements will in general be small in emerging growth markets (compared to corresponding mature markets).
Terminal-equipment related OpEx: is the cost associated by procuring terminals equipment (i.e, handsets, smartphones, data cards, etc.). In the past (prior to 2008) it was fairly common that OpEx from procuring and revenues from selling terminals were close to a zero-sum game. In other words the cost made for the operator of procuring terminals was pretty much covered by re-selling them to their customer base. This cost structure element is another heavy weight and vary from 10% to 20% of the OpEx with an average in mature European markets of 17%. Terminal-related cost on average amounts to ca. 11% of the Revenue (in mature markets). Most operators in emerging growth markets don’t massively procure, re-sell and subsidies handsets, as is the case in many mature markets. Typically handsets and devices in emerging markets will be supplied by a substantial 2nd hand gray and black market readily available.
Personnel Cost: amounts to between 6% to 15% of the Total OpEx with a best-practice share of around the 10%. The ones who believe that this ratio is lower in emerging markets might re-think their impression. In my experience emerging growth markets (including the ones in Eastern & Central Europe) have a lower unit personnel cost but also tends to have much larger organizations. This leads to many emerging growth markets operators having a personnel cost share that is closer to the 15% than to the 10% or lower. On average personnel cost should be below 10% of revenue with best practice between 5% and 8% of the Revenue.
Technology Cost (Network & IT):includes all technology related OpEx for both Network and Information Technology. Personnel-related technology OpEx (prior to capitalization ) is accounted for in the above Personnel Cost Category and would typically be around 30% of the personnel cost pending on outsourcing level and organizational structure. Emerging markets in Central & Eastern Europe historical have had higher technology related personnel cost than mature markets. In general this is attributed to high-quality relative low-cost technology staff leading to less advantages in outsourcing technology functions. As Technology OpEx is the most frequent “victim” of efficiency initiatives, lets just have a look at how the anatomy of the Technology Cost Structure looks like:
Technology Cost (Network & IT) – continued: Although, above Chart (i.e., taken from my 2012 Keynote at the Broadband MEA 2012, Dubai “Ultra-efficient network factory: Network sharing and other means to leapfrog operator efficiencies”) emphasizes a Mature Market View, emerging markets cost distribution does not differ that much from the above with a few exceptions. In Emerging Growth Markets with poor electrification rates diesel generators and the associated diesel full will strain the Energy Cost substantially. As the biggest exposure to poor electrical grid (in emerging markets) in general tend to be in Rural and Sub-Urban areas it is a particular OpEx concern as the emerging market operators expands towards Rural Areas to capture the additional subscriber potential present there. Further diesel fuel has on average increased with 10% annually (i..e, over the least 10 years) and as such is a very substantial Margin and Profitability risk if a very large part of the cellular / mobile network requires diesel generators and respective fuel. Obviously, “Rental & Leasing” as well as “Service & Maintenance” & “Personnel Cost” would be positively impacted (i.e., reduced) by Network Sharing initiatives. Best practices Network Sharing can bring around 35% OpEx savings on relevant cost structures. For more details on benefits and disadvantages (often forgotten in the heat of the moment) see my Blog “The ABC of Network Sharing – The Fundamentals”. In my experience one of the greatest opportunities in Emerging Growth Markets for increased efficiency are in the Services part covering Maintenance & Repair (which obviously also incudes field maintenance and spare part services).
Other Cost: typically covers the rest of OpEx not captured by the above specific items. It can also be viewed as overhead cost. It is also often used to “hide” cost that might be painful for the organization (i.e., in terms of authorization or consequences of mistakes). In general you will find a very large amount of smaller to medium cost items here rather than larger ones. Best practices should keep this below 10% of total OpEx and ca. 5% of Revenues. Much above this either means mis-categorization, ad-hoc projects, or something else that needs further clarification.
So how does this help us compare a Mature Mobile Market with an Emerging Growth Market?
As already mentioned in the description of the above cost structure categories particular Market Invest and Terminal-equipment Cost are items that tend to be substantially lower for emerging market operators or entirely absent from their cost structures.
Lets assume our average mobile operator in an average mature mobile market (in Western Europe) have a Margin of 36%. In its existing (OpEx) cost structure they spend 15% of Revenue on Market Invest of which ca. 53% goes to subscriber acquisition (i.e., SAC cost category), 40% on subscriber retention (SRC) and another 7% for other marketing expenses. Further, this operator has been subsidizing their handset portfolio (i.e., Terminal Cost) which make up another 10% of the Revenue.
Our Average Operator comes up with the disruptive strategy to remove all SAC and SRC from their cost structure and stop procuring terminal equipment. Assuming (and that is a very big one in a typical western European mature market) that revenue remains at the same level, how would this average operator fare?
Removing SAC and SRC, which was 14% of the Revenue will improve the Margin adding another 14 percentage points. Removing terminal procurement from its cost structure leads to an additional Margin jump of 10 percentage points. The final result is a Margin of 60% which is fairly close to some of the highest margins we find in emerging growth markets. Obviously, completely annihilating Market Invest might not be the most market efficient move unless it is a market-wide initiative.
Albeit the example might be perceived as a wee bit academic, it serves to illustrate that some of the larger margin differences we observe between mobile operators in mature and emerging growth markets can be largely explain by differences in the basic cost structure, i..e, the lack of substantial subscriber acquisition and retention costs as well as not procuring terminals does offer advantages to the emerging market business model.
However, it also means that many operators in emerging markets have little OpEx flexibility, in the sense of faster OpEx reduction opportunities once mobile margin reduces due to for example slowing revenue growth. This typical becomes a challenge as mobile penetration starts reaching saturation and as ARPU reduces due to diminishing return on incremental customer acquisition.
There is not much substantial OpEx flexibility (i..e, market invest & terminal procurement) in Emerging Growth Markets mobile accounts. This adds to the challenge of avoiding profitability squeeze and margin exposure by quickly scaling back OpEx.
This is to some extend different from mature markets that historically had quiet a few low hanging fruits to address before OpEx efficiency and reduction became a real challenge. Though ultimately it does become a challenge.
Back to Profitability with a Vengeance.
So it is all pretty simple! … leave out Market Invest and Terminal Procurement … Then add that we typically have to do with Lower GDP countries which conventional wisdom would expect also to have lower Opex (or Cost in general) combined with better population scale .. isn’t that really what makes for a great emerging growth market Mobile Margin?
Hmmm … Albeit Compelling ! ? … For the ones (of us) who would think that the cost would scale nicely with GDP and therefor a Low GDP Country would have a relative Lower Cost Base, well …
In the Chart above the Y-axis is depicted with logarithmic scaling in order to provide a better impression of the data points across the different economies. It should be noted that throughout the years 2007 to 2013 (note: 2013 data is shown above) there is no correlation between a countries mobile Opex, as estimated by Revenue – EBITDA, and the GDP.
Well … GDP really doesn’t provide the best explanation (to say the least)! … So what does then?
I have carried out multi-linear regression analysis on the available data from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” datasets between the years 2007 to 2013. The multi-linear regression approach is based on year-by-year analysis of the data with many different subsets & combination of data chosen including adding random data.
I find that the best description (R-square 0.73, F-Ratio of 30 and p-value(s) <0.0001) of the 48 country’s data on Opex. The amount of data points used in the multi-regression is at least 48 for each parameter and that for each of the 7 years analyzed. The result of the (preliminary) analysis is given by the following statistically significant parameters explaining the Mobile Market OpEx:
Population – The higher the size of the population, proportional less Mobile Market Opex is spend (i.e., scale advantage).
Penetration – The higher the mobile penetration, proportionally less Mobile Market Opex is being spend (i.e., scale advantage and the incremental penetration at an already high penetration would have less value thus less Opex should be spend).
Users (i..e., as measured by subscriptions) – The more Users the higher the Mobile Market Opex (note: prepaid ratio has not been found to add statistical significance).
ARPU (Average Revenue Per User) – The higher the ARPU, the higher will the Mobile Market Opex be.
If I leave out ARPU, GDP does enter as a possible descriptive candidate although the overall quality of the regression analysis suffers. However, it appears that the GDP and ARPU cannot co-exist in the analysis. When Mobile Market ARPU data are included, GDP becomes non-significant. Furthermore, a countries Surface Area, which I previously believed would have a sizable impact on a Mobile Market’s OpEx, also does not enter as a significant descriptive parameter in this analysis. In general the Technology related OpEx is between 15% to 25% (maximum) of the Total OpEx and out that possible 40% to 60% would be related to sites that would be needed to cover a given surface area. This might no be significant enough in comparison to the other parameters or simply not a significant factor in the overall country related mobile OpEx.
I had also expected 3G-UMTS to have had a significant contribution to the Opex. However this was not very clear from the analysis either. Although in the some of the earlier years (2005 – 2007), 3G does enter albeit not with a lot of weight. In Western Europe most incremental OpEx related to 3G has been absorb in the existing cost structure and very little (if any) incremental OpEx would be visible particular after 2007. This might not be the case in most Emerging Markets unless they can rely on UMTS deployments at 900 MHz (i.e., the traditional GSM band). Also the UMTS 900 solution would only last until capacity demand require the operators to deploy UMTS 2100 (or let their customers suffer with less mobile data quality and keep the OpEx at existing levels). In rural areas (already covered by GSM at 900 MHz) the 900 MHz UMTS deployment option may mitigate incremental OpEx of new site deployment and further encourage rural active network sharing to allow for lower cost deployment and providing rural populations with mobile data and internet access.
The Population Size of a Country, the Mobile Penetration and the number of Users and their ARPU (note last two basically multiplies up to the revenue) are most clearly driving a mobile markets Opex.
Philippines versus Germany – Revenue, Cost & Profitability.
Philippines in 2013 is estimated to have a population of ca. 100 Million compared to Germany’s ca. 80 Million. The Urban population in Germany is 75% taking up ca. 17% of the German surface area (ca. 61,000 km2 or a bit more than Croatia). Comparison this to Philippines 50% urbanization that takes up up only 3% (ca. 9,000 km2 or equivalent to the surface area of Cyprus). Germany surface area is about 20% larger than Philippines (although the geographies are widely .. wildly may be a better word … different, with the Philippines archipelago comprising 7,107 islands of which ca. 2,000 are inhabited, making the German geography slightly boring in comparison).
In principle if all I care about is to cover and offer services to the urban population (supposedly the ones with the money?) I only need to cover 9 – 10 thousand square kilometer in the Philippines to capture ca. 50 Million potential mobile users (or 5,000 pop per km2), while I would need to cover about 6 times that amount of surface area to capture 60 million urban users in Germany (or 1,000 pop per km2). Even when taking capacity and quality into account, my Philippine cellular network should be a lot smaller and more efficient than my German mobile network. If everything would be equal, I basically would need 6 times more sites in Germany compared to Philippines. Particular if I don’t care too much about good quality but just want to provide best effort services (that would never work in Germany by the way). Philippines would win any day over Germany in terms of OpEx and obviously also in terms of capital investments or CapEx. It does help the German Network Economics that the ARPU level in Germany is between 4 times (in 2003) to 6 times (in 2013) higher than in Philippines. Do note that the two major Germany mobile operators covers almost 100% of the population as well as most of the German surface area and that with a superior quality of voice as well as mobile broadband data. This does not true hold true for Philippines.
In 2003 a mobile consumer in Philippines would spend on average almost 8 US$ per month for mobile services. This was ca. 4x lower than a German customer for that year. The 2003 ARPU of the Philippines roughly corresponded to 10% of the GDP per Capita versus 1.2% of the German equivalent. Over the 10 Years from 2003 to 2013, ARPU dropped 60% in Philippine and by 2013 corresponded to ca. 1.5% of GDP per Capita (i.e., a lot more affordable propositions). The German 2013 ARPU to GDP per Capita ratio was 0.5% and its ARPU was ca. 40% lower than in 2003.
The Philippines ARPU decline and Opex increase over the 10 year period led to a Margin drop from 64% to 45% (19% drop!) and their Margin is still highly likely to fall further in the near to medium-term. Despite the Margin drop Philippines still made a PHP26 Billion more EBITDA in 2013 than compared to 2003 (ca. 45% more or equivalent compounded annual growth rate of 3.8%).
Germany had ca. 3x more mobile subscribers compared to Philippines.
German Mobile Revenue was 14x higher than Philippines.
German EBITDA was 9x higher than that of Philippines.
German OpEx was 23x higher than that of Philippines Mobile Industry.
Mobile Margin of the Philippines was 64% versus 42% of Germany.
Germany’s GPD per Capita (in US$) was 35 times larger than that of Philippines.
Germany’s mobile ARPU was 4 times higher than that of Philippines.
in 2013 (+ 10 Years)
Philippines & Germany have almost the same amount of mobile subscriptions.
Germany Mobile Revenue was 6x higher than Philippines.
German EBITDA was only 5x higher than that of Philippines.
German OpEx was 6x higher than Mobile OpEx in Philippines (and German OpEx was at level with 2003).
Mobile Margin of the Philippines dropped 19% to 45% compared to 42% of Germany (essential similar to 2003).
In local currencies, Philippines increased their EBITDA with ca. 45%, Germany remain constant.
Both Philippines and Germany has lost 11% in absolute EBITDA between the 10 Year periods maximum and 2013.
Germany’s GDP per Capita (in US$) was 14 times larger than that of the Philippines.
Germany’s ARPU was 6 times higher than that of Philippines.
In the Philippines, mobile revenues have grown with 7.4% per anno (between 2003 and 2013) while the corresponding mobile OpEx grew with 12% and thus eroding margin massively over the period as increasingly more mobile customers were addressed. In Philippines, the 2013 OpEx level was 3 times that of 2003 (despite one major network consolidation and being an essential duopoly after the consolidation). In Philippines over this period the annual growth rate of mobile users were 17% (versus Germany’s 6%). In absolute terms the number of users in Germany and Philippines were almost the same in 2013, ca. 115 Million versus 109 Million. In Germany over the same period Financial growth was hardly present although more than 50 Million subscriptions were added.
When OpEx grows faster than Revenue, Profitability will suffer today & even more so tomorrow.
Mobile capital investments (i.e., CapEx) over the period 2003 to 2013 was for Germany 5 times higher than that of Philippines (i.e., remember that Germany also needs at least 5 – 6 times more sites to cover the Urban population) and tracks at a 13% Capex to Revenue ratio versus Philippines 20%.
The stories of Mobile Philippines and of Mobile Germany are not unique. Likewise examples can be found in Emerging Growth Markets as well as Mature Markets.
Can Mature Markets learn or even match (keep on dreaming?) from Emerging Markets in terms of efficiency? Assuming such markets really are efficient of course!
As logic (true or false) would dictate given the relative low ARPUs in emerging growth markets and their correspondingly high margins, one should think that such emerging markets are forced to run their business much more efficient than in Mature Markets. While compelling to believe this, the economical data would indicate that most emerging growth markets have been riding the subscriber & revenue growth band wagon without too much thoughts to the OpEx part … and Frankly why should you care about OpEx when your business generates margins much excess of 40%? Well … it is (much) easier to manage & control OpEx year by year than to abruptly “one day” having to cut cost in panic mode when growth slows down the really ugly way and OpEx keeps increasing without a care in the world. Many mature market operators have been in this situation in the past (e.g., 2004 – 2008) and still today works hard to keep their margins stable and profitability from declining.
Most Companies will report both Revenues and EBITDA on quarterly and annual basis as both are key financial & operational indicators for growth. They tend not report Opex but as seen from above that’s really not a problem to estimate when you have Revenue and EBITDA (i.e., OpEx = Revenue – EBITDA).
Thus, had you left the European Telco scene (assuming you were there in the first place) for the last 10 years and then came back you might have concluded that not much have happened in your absence … at least from a profitability perspective. Germany was in 2013 almost at its Ebitda margin level of 2003. Of course as the ones who did not take a long holiday knows those last 10 years were far from blissful financial & operational harmony in the mature markets where one efficiency program after the other struggled to manage, control and reduce Operators Operational Expenses.
However, over that 10-year period Germany added 50+ Million mobile subscriptions and invested more than 37 Billion US$ into the mobile networks from T-Deutschland, Vodafone, E-plus and Telefonica-O2. The mobile country margin over the 10-year period has been ca. 43% and the Capex to Revenue ratio ca. 13%. By 2013 the total amount of mobile subscription was in the order of 115 Million out of a population of 81 Million (i.e., 54 Million of the German population is between 15 and 64 years of age). The observant numerologist would have realized that there are many more subscriptions than population … this is not surprising as it reflects that many subscribers are having multiple different SIM cards (as opposed to cloned SIMs) or subscription types based on their device portfolio and a host of other reasons.
All Wunderbar! … or? .. well not really … Take a look at the revenue and profitability over the 10 year period and you will find that no (or very very little) revenue and incremental profitability has been gained over the period from 2003 to 2013. AND we did add 80+% more subscriptions to the base!
Here is the Germany Mobile development over the period;
Apart from adding subscribers, having modernized the mobile networks at least twice over the period (i.e, CapEx with little OpEx impact) and introduced LTE into the German market (i.e., with little additional revenue to show for it) not much additional value has been added. It is however no small treat what has happen in Germany (and in many other mature markets for that matter). Not only did Germany almost double the mobile customers (in terms of subscriptions), over the period 3G Nodes-B’s were over-layed across the existing 2G network. Many additional sites were added in Germany as the fundamental 2G cellular grid was primarily based on 900 MHz and to accommodate the higher UMTS frequency (i.e., 2100 MHz) more new locations were added to provide a superior 3G coverage (and capacity/quality). Still Germany managed all this without increasing the Mobile Country OpEx across the period (apart from some minor swings). This has been achieved by a tremendous attention to OpEx efficiency with every part of the Industry having razor sharp attention to cost reduction and operating at increasingly efficiency.
Philippines story is a Fabulous Story of Growth (as summarized above) … and of Profitability & Margin Decline.
Philippines today is in effect a duopoly with PLDT having approx. 2/3 of the mobile market and Globe the remaining 1/3. During the period the Philippine Market saw Sun Cellular being acquired and merged by PLDT. Further, 3G was deployed and mobile data launched in major urban areas. SMS revenues remained the largest share of non-voice revenue to the two remaining mobile operators PLDT and Globe. Over the period 2003 to 2013, the mobile subscriber base (in terms of subscriptions) grew with 16% per anno and the ARPU fell accordingly with 10% per anno (all measured in local currency). All-in-all safe guarding a “healthy” revenue increase over the period from ca. 93 Billion PHP in 2003 to 190 Billion PHP in 2013 (i.e., a 65% increase over the period corresponding to a 5% annual growth rate).
However, the Philippine market could not maintain their relative profitability & initial efficiency as the mobile market grew.
So we observe (at least) two effects (1) Reduction in ARPU as market is growing & (2) Increasing Opex cost to sustain the growth in the market. As more customers are added to a mobile network the return on thus customers increasingly diminishes as network needs to be massively extended capturing the full market potential versus “just” the major urban potential.
Mobile Philippines did become less economical efficient as its scale increases and ARPU dropped (i.e., by almost 70%). This is not an unusual finding across Emerging Growth Markets.
As I have described in my previous Blog “SMS – Assimilation is inevitable, Resistance is Futile!”, Philippines mobile market has an extreme exposure to SMS Revenues which amounts to more than 35% of Total Revenues. Particular as mobile data and smartphones penetrate the Philippine markets. As described in my previous Blog, SMS Services enjoy the highest profitability across the whole range of mobile services we offer the mobile customer including voice. As SMS is being cannibalized by IP-based messaging, the revenue will decline dramatically and the mobile data revenue is not likely to catch up with this decline. Furthermore, profitability will suffer as the the most profitable service (i.e., SMS) is replaced by mobile data that by nature has a different profitability impact compared to simple SMS services.
Philippines do not only have a substantial Margin & EBITDA risk from un-managed OpEx but also from SMS revenue cannibalization (a la KPN in the Netherlands and then some).
Let us compare the ARPU & Opex development for Philippines (above Chart) with that of Germany over the same period 2003 to 2013 (please note that the scale of Opex is very narrow)
Mobile Germany managed their Cost Structure despite 40+% decrease in ARPU and as another 60% in mobile penetration was added to the mobile business. Again similar trend will be found in most Mature Markets in Western Europe.
One may argue (and not being too wrong) that Germany (and most mature mobile markets) in 2003 already had most of its OpEx bearing organization, processes, logistics and infrastructure in place to continue acquiring subscribers (i.e., as measured in subscriptions). Therefor it have been much easier for the mature market operators to maintain their OpEx as they continued to grow. Also true that many emerging mobile markets did not have the same (high) deployment and quality criteria, as in western mature markets, in their initial network and service deployment (i.e., certainly true for the Philippines as is evident from the many Regulatory warnings both PLDT and Globe received over the years) providing basic voice coverage in populated areas but little service in sub-urban and rural areas.
Most of the initial emerging market networks has been based on coarse (by mature market standards) GSM 900 MHz (or CDMA 850 MHz) grids with relative little available capacity and indoor coverage in comparison to population and clutter types (i.e., geographical topologies characterized by their cellular radio interference patterns). The challenge is, as an operator wants to capture more customers, it will need to build out / extend its mobile network in the areas those potential or prospective new customers live and work in. From a cost perspective sub-urban and rural areas in emerging markets are not per se lower cost areas despite such areas in general being lower revenue areas than their urban equivalents. Thus, as more customers are added (i.e., increased mobile penetration) proportionally more cost are generated than revenue being capture and the relative margin will decline. … and this is how the Ugly-cost (or profitability tail) is created.
I just cannot write about profitability and cost structure without throwing the Ugly-(cost)-Tail on the page.I strongly encourage all mobile operators to make their own Ugly-Tail analysis. You will find more details of how to remedy this Ugliness from your cost structure in “The ABC of Network Sharing – The Fundamentals”.
In Western Europe’s mature mobile markets we find that more than 50% of our mobile cellular sites captures no more than 10% of the Revenues (but we do tend to cover almost all surface area several times unless the mobile operators have managed to see the logic of rural network sharing and consolidated those rural & sub-urban networks). Given emerging mobile markets have “gone less over board” in terms of lowest revenue un-profitable network deployments in rural areas you will find that the number of sites carrying 10% of less of the revenue is around 40%. It should be remembered that the rural populations in emerging growth markets tend to be a lot larger than in of that in mature markets and as such revenue is in principle spread out more than what would be the case in mature markets.
Population & Mobile Statistics and Opex Trends.
The following provides a 2013 Summary of Mobile Penetration, 3G Penetration (measured in subscriptions), Urban Population and the corresponding share of surface area under urban settlement. Further to guide the eye the 100% line has been inserted (red solid line), a red dotted line that represents the share of the population that is between 15 and 64 years of age (i.e., who are more likely to afford a mobile service) and a dashed red line providing the average across all the 43 countries analyzed in this Blog.
Sources: United Nations, Department of Economic & Social Affairs, Population Division. The UN data is somewhat outdated though for most data points across emerging and mature markets changes have been minor. Mobile Penetration is based on Pyramid Research and Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line).
There is a couple of points (out of many) that can be made on the above data;
There are no real emerging markets any longer in the sense of providing basic mobile telephone services such as voice and messaging.
For mobile broadband data via 3G-UMTS (or LTE for that matter), what we tend to characterize as emerging markets are truly emerging or in some case nascent (e.g., Algeria, Iraq, India, Pakistan, etc..).
All mature markets have mobile penetration rates way above 100% with exception of Canada, i.e., 80% (i.e., though getting to 100% in Canada might be a real challenge due to a very dispersed remaining 20+% of the population).
Most emerging markets are by now covering all urban areas and corresponding urban population. Many have also reach 100% mobile penetration rates.
Most Emerging Markets are lagging Western Mature Markets in 3G penetration. Even providing urban population & urban areas with high bandwidth mobile data is behind that of mature markets.
Size & density does matter … in all kind of ways when it comes to the economics of mobile networks and the business itself.
In Australia I only need to cover ca. 40 thousand km2 (i.e., 0.5% of the total surface area and a bit less than the size of Denmark) to have captured almost 90% of the Australian population (e.g., Australia’s total size is 180+ times that of Denmark excluding Greenland). I frequently hear my Australian friends telling me how Australia covers almost 100% of the population (and I am sure that they cover more area than is equivalent to Denmark too) … but without being (too) disrespectful that record is not for Guinness Book of Records anytime soon. in US (e.g., 20% more surface area than Australia) I need to cover in almost 800 thousand km2 (8.2% of surface area or equivalent to a bit more than Turkey) to capture more than 80% of the population. In Thailand I can only capture 35% of the population by covering ca. 5% of the surface area or a little less than 30 thousand km2 (approx. the equivalent of Belgium). The remaining of 65% of the Thai population is rural-based and spread across a much larger surface area requiring extensive mobile network to provide coverage to and capture additional market share outside the urban population.
So in Thailand I might need a bit less cell sites to cover 35% of my population (i.e., 22M) than in Australia to cover almost 90% of the population (i.e., ca. 21M). That’s pretty cool economics for Australia which is also reflected in a very low profitability risk score. For Thailand (and other countries with similar urban demographics) it is tough luck if they want to reach out and get the remaining 65% of their population. The geographical dispersion of the population outside urban areas is very wide and increasing geographical area is required to be covered in order to catch this population group. UMTS at 900 MHz will help to deploy economical mobile broadband, as will LTE in the APT 700 MHz band (being it either FDD Band 28 or TDD Band 44) as the terminal portfolio becomes affordable for rural and sub-urban populations in emerging growth markets.
In Western Europe on average I can capture 77% of my population (i..e, the urban pop) covering 14.2% of the surface area (i.e., average over markets in this analysis), This is all very agreeable and almost all Western European countries cover their surface areas to at least 80% and in most cases beyond that (i.e., it’s just less & easier land to cover though not per see less costly). In most cases rural coverage is encourage (or required) by the mature market license regime and not always a choice of the mobile operators.
Before we look in depth to the growth (incl. positive as well as negative growth), lets first have a peek at what has happened to the mobile revenue in terms of ARPU and Number of Mobile User and the corresponding mobile penetration over the period 2007 to 2013.
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data were used to calculated the growth of ARPU as compounded annual growth rate between 2007 to 2013 and the annual growth rate between 2012 and 2013. Since 2007 the mobile ARPUs have been in decline and to make matters worse the decline has even accelerated rather than slowed down as markets mobile penetration saturated.
Source: Mobile Penetrations taken from Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data .Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line). It s interesting to observe that most emerging growth markets are now where the mature markets were in 2007 in terms of mobile penetration.
Apart from a very few markets, ARPU has been in a steady decline since 2007. Further in many countries the ARPU decline has even accelerated rather than slowed down. From most mature markets the conclusion that we can draw is that there are no evidence that mobile broadband data (via 3G-UMTS or LTE) has had any positive effect on ARPU. Although some of the ARPU decline over the period in mature markets (particular European Union countries) can be attributed to regulatory actions. In general as soon a country mobile penetration reaches 100% (in all effect reaches the part of the population 15-64 years of age) ARPU tends to decline faster rather than slowing down. Of course one may correctly argue that this is not a big issue as long as the ARPU times the Users (i.e., total revenue) remain growing healthily. However, as we will see that is yet another challenge for the mobile industry as also the total revenue in mature markets also are in decline on a year by year basis. Given the market, revenue & cost structures of emerging growth markets, it is not unlikely that they will face similar challenges to their mobile revenues (and thus profitability). This could have a much more dramatic effect on their overall mobile economics & business models than what has been experienced in the mature markets which have had a lot more “cushion” on the P&Ls to defend and even grow (albeit weakly) their profitability. It is instructive to see that the most emerging growth markets mobile penetrations have reached the levels of Mature Markets in 2007. Combined with the introduction and uptake of mobile broadband data this marks a more troublesome business model phase than what these markets have experienced in the past.Some of the emerging growth market have yet to introduce 3G-UMTS, and some to leapfrog mobile broadband by launching LTE. Both events, based on lessons learned from mature markets, heralds a more difficult business model period of managing cost structures while defending revenues from decline and satisfy customers appetite for mobile broadband internet that cannot be supported by such countries fixed telecommunications infrastructures.
For us to understand more profoundly where our mobile profitability is heading it is obviously a good idea to understand how our Revenue and OpEx is trending. In this Section I am only concerned about the Mobile Market in Country and not the individual mobile operators in the country. For that latter (i.e., Operator Profitability) you will find a really cool and exiting analytic framework in the Section after this. I am also not interested (in this article) in modeling the mobile business bottom up (been there & done that … but that is an entirely different story line). However, I am interested and I am hunting for some higher level understanding and a more holistic approach that will allow me to probabilistically (by way of Bayesian analysis & ultimately inference) to predict in which direction a given market is heading when it comes to Revenue, OpEx and of course the resulting EBITDA and Margin. The analysis I am presenting in this Section is preliminary and only includes compounded annual growth rates as well as the Year-by-Year growth rates of Revenue and OpEx. Further developments will include specific market & regulatory developments as well to further improve on the Bayesian approach. Given the wealth of data accumulated over the years from the Bank of America Merrill Lynch (BoAML) Global Wireless Matrix datasets it is fairly easy to construct & train statistical models as well as testing those consistent with best practices.
The Chart below comprises 48 countries Revenue & OpEx growth rates as derived from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” dataset (note: BoAML data available in this analysis goes back to 2003). Out of the 48 Countries, 23 countries have an Opex compounded annual growth rate higher than the corresponding Revenue growth rate. Thus, it is clear that those 23 countries are having a higher risk of reduced margin and strained profitability due to over-proportionate growth of OpEx. Out of the 23 countries with high or very high profitability risk, 11 countries have been characterized in macro-economical terms as emerging growth markets (i.e., China, India, Indonesia, Philippines, Egypt, Morocco, Nigeria, Russia, Turkey, Chile, Mexico) the remaining 12 countries can be characterized as mature markets in macro-economical terms (i.e., New Zealand, Singapore, Austria, Belgium, France, Greece, Spain, Canada, South Korea, Malaysia, Taiwan, Israel). Furthermore, 26 countries had a higher Opex growth between 2012 and 2013 than their revenues and is likely to be trending towards dangerous territory in terms of Profitability Risk.
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. The Compounded Annual Growth Rate (CAGR) is calculated with X being Revenue and Opex. Y-axis scale is from -25% to +25% (i.e., similar to the scale chosen in the Year- by-Year growth rate shown in the Chart below).
With few exceptions one does not need to read the countries names on the Chart above to immediately see where we have the Mature Markets with little or negative growth and where what we typically call emerging growth markets are located.
As the above Chart clearly illustrate the mobile industry across different types of markets have an increasing challenge to deliver profitable growth and if the trend continues to keep their profitability period!
Opex grows faster than Mobile Operator’s can capture Revenue … That’s a problem!
In order gauge whether the growth dynamics of the last 7 years is something to be concerned about (it is! … it most definitely is! but humor me!) … it is worthwhile to take a look at the year by year growth rate trends (i.e. as CAGR only measures the starting point and the end point and “doesn’t really care” about what happens in the in-between years).
Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. Year on Year growth is calculated and is depicted in the Chart above. Y-axis scale is from -25% to +25%. Note that the Y-scales in the Year-on-Year Growth Chart and the above 7-Year CAGR Growth Chart are the same and thus directly comparable.
From the Year on Year Growth dynamics compared to the compounded 7-year annual growth rate, we find that Mature Markets Mobile Revenues decline has accelerated. However, in most cases the Mature Market OpEx is declining as well and the Control & Management of the cost structure has improved markedly over the last 7 years. Despite the cost structure management most Mature Markets Revenue have been declining faster than the OpEx. As a result Profitability Squeeze remains a substantial risk in Mature Markets in general.
In almost all Emerging Growth Markets the 2013 to 2012 revenue growth rate has declined in comparison with the compounded annual growth rate. Not surprising as most of those markets are heading towards 100% mobile penetration (as measured in subscriptions). OpEx growth remains a dire concern for most of the emerging growth markets and will continue to squeeze emerging markets profitability and respective margins. There is no indication (in the dataset analyzed) that OpEx is really under control in Emerging Growth Markets, at least to the same degree as what is observed in the Mature Markets (i.e., particular Western Europe). What further adds to the emerging markets profitability risk is that mobile data networks (i.e., 3G-UMTS, HSPA+,..) and corresponding mobile data uptakes are just in its infancy in most of the Emerging Growth Markets in this analysis. The networks required to sustain demand (at a reasonable quality) are more extensive than what was required to provide okay-voice and SMS. Most of the emerging growth markets have no significant fixed (broadband data) infrastructure and in addition poor media distribution infrastructure which can relieve the mobile data networks being built. Huge rural populations with little available ARPU potential but a huge appetite to get connected to internet and media will further stress the mobile business models cost structure and sustainable profitability.
This argument is best illustrated by comparing the household digital ecosystem evolution (or revolution) in Western Europe with the projected evolution of Emerging Growth Markets.
Above Chart illustrates the likely evolution in Home and Personal Digital Infrastructure Ecosystem of an emerging market’s Household (HH). Particular note that the amount of TV Displays are very low and much of the media distribution is expected to happen over cellular and wireless networks. An additional challenge is that the fixed broadband infrastructure is widely lagging in many emerging markets (in particular in sub-urban and rural areas) increasing the requirements of the mobile network in those markets. It is compelling to believe that we will witness a completely different use case scenarios of digital media consumption than experienced in the Western Mature Markets. The emerging market is not likely to have the same degree of mobile/cellular data off-load as experienced in mature markets and as such will strain mobile networks air-interface, backhaul and backbone substantially more than is the case in mature markets. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
Same as above but projection for Western Europe. In comparison with Emerging Markets a Mature Market Household (HH) has many more TV as wells as a substantially higher fixed broadband penetration offering high-bandwidth digital media distribution as well as off-load optionality for mobile devices via WiFi. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
Mobile Market Profit Sustainability Risk Index
The comprehensive dataset from Bank of America Merrill Lynch Global Wireless Matrix allows us to estimate what I have chosen to call a Market Profit Sustainability Risk Index. This Index provides a measure for the direction (i.e., growth rates) of Revenue & Opex and thus for the Profitability.
The Chart below is the preliminary result of such an analysis limited to the BoAML Global Wireless Matrix Quarter 1 of 2014. I am currently extending the Bayesian Analysis to include additional data rather than relying only on growth rates of Revenue & Opex, e.g., (1) market consolidation should improve the cost structure of the mobile business, (2) introducing 3G usually introduces a negative jump in the mobile operator cost structure, (3) mobile revenue growth rate reduces as mobile penetration increases, (4) regulatory actions & forces will reduce revenues and might have both positive and negative effects on the relevant cost structure, etc.…
So here it is! Preliminary but nevertheless directionally reasonable based on Revenue & Opex growth rates, the Market Profit Sustainability Risk Index over for 48 Mature & Emerging Growth Markets worldwide:
The above Market Profit Sustainability Risk Index is using the following risk profiles
Very High Risk (index –5): (i.e., for margin decline): (i) Compounded Annual Growth Rate (CAGR) between 2007 and 2013 of Opex was higher than equivalent for Revenue AND (ii) Year-on-Year (YoY) Growth Rate 2012 to 2013 of Opex higher than that of Revenue AND (iii) Opex Year-on-Year 2012 to 2013 Growth Rate is higher than the Opex CAGR over the period 2007 to 2013.
High Risk (index –3): Same as above Very High Risk with condition (iii) removed ORYoY Revenue Growth 2012 to 2013 lower than the corresponding Opex Growth.
Medium Risk (index –2): CAGR of Revenue lower than CAGR of Opex but last year (i.e., 2012 t0 2013) growth rate of Revenue higher than that of Opex.
Low Risk (index 1): (i) CAGR of Revenue higher than CAGR of Opex AND (ii) YoY Revenue Growth higher than Opex Growth but lower than the inflation of the previous year.
Very Low Risk (index 3): Same as above Low Risk with YoY Revenue Growth Rate required to be higher than the Opex Growth with at least the previous year’s inflation rate.
The Outlook for Mature Markets are fairly positive as most of those Market have engaged in structural cost control and management for the last 7 to 8 years. Emerging Growth Markets Profit Sustainability Risk Index are cause for concern. As the mobile markets are saturating it usually results in lower ARPU and higher cost to reach the remaining parts of the population (often “encouraged” by regulation). Most Emerging Growth markets have started to introduce mobile data, which is likely to result in higher cost-structure pressure & with traditional revenue streams under pressure (if history of Mature Markets are to repeat itself in emerging growth markets). The Emerging Growth Markets have had little incentive (in the past) to focus on cost structure control and management, due to the exceedingly high margins that they historically could present with their legacy mobile services (i.e., Voice & SMS) and relative light networks (as always in comparison to Mature Markets).
Cautionary note is appropriate. All the above are based on the Mobile Market across the world. There are causes and effects that can move a market from having a high risk profile to a lower. Even if I feel that the dataset supports the categorization it remains preliminary as more effects should be included in the current risk model to add even more confidence in its predictive power. Furthermore, the analysis is probabilistic in nature and as such does not claim to carve in stone the future. All the Index claims to do is to indicate a probable direction of the profitability (as well as Revenue & OpEx). There are several ways that Operators and Regulatory Authorities might influence the direction of the profitability changing Risk Exposure (in the Wrong as well as in the Right Direction)
Furthermore, it would be wrong to apply the Market Profit Sustainability Risk Index to individual mobile operators in the relevant markets analyzed here. The profitability dynamics of individual mobile operators are a wee bit more complicated, albeit some guidelines and predictive trends for their profitability dynamics in terms of Revenue and Opex can be defined. This will all be revealed in the following Section.
Operator Profitability – the Profitability Math.
We have seen that the Margin M an be written as
with E, R and O being EBITDA, REVENUE and OPEX respectively.
However, much more interesting is that it can also be written as a function of subscriber share
being valid forwith being the margin and the subscriber market share can be found between 0% to 100%. The rest will follow in more details below, suffice to say that as the subscriber market share increases the Margin (or relative profitability) increases as well although not linearly (if anyone would have expected that ).
Before we get down and dirty on the math lets discuss Operator Profitability from a higher level and in terms of such an operators subscriber market share (i.e., typically measured in subscriptions rather than individual users).
In the following I will show some Individual Operator examples of EBITDA Margin dynamics from Mature Markets limited to Western Europe. Obviously the analysis and approach is not limited emerging markets and can (have been) directly extended to Emerging Growth Markets or any mobile market for that matter. Again BoAML Global Matrix provides a very rich data set for applying the approach described in this Blog.
It has been well established (i.e., by un-accountable and/or un-countable Consultants & Advisors) that an Operator’s Margin correlates reasonably well with its Subscriber Market Share as the Chart below illustrates very well. In addition the Chart below also includes the T-Mobile Netherlands profitability journey from 2002 to 2006 up to the point where Deutsche Telekom looked into acquiring Orange Netherlands. An event that took place in the Summer of 2007.
I do love the above Chart (i.e., must be the physicist in me?) as it shows that such a richness in business dynamics all boiled down to two main driver, i.e., Margin & Subscriber Market Shared.
So how can an Operator strategize to improve its profitability?
Let us take an Example
Here is how we can think about it in terms of Subscriber Market Share and EBITDA as depicted by the above Chart. In simple terms an Operator have a combination of two choices (Bullet 1 in above Chart) Improve its profitability through Opex reductions and making its operation more efficient without much additional growth (i.e., also resulting in little subscriber acquisition cost), it can improve its ARPU profile by increasing its revenue per subscriber (smiling a bit cynical here while writing this) again without adding much in additional market share. The first part of Bullet 1 has been pretty much business as usual in Western Europe since 2004 at least (unfortunately very few examples of the 2nd part of Bullet 1) and (Bullet 2 in above Chart) The above “Margin vs. Subscriber Market Share” Chart indicates that if you can acquire the customers of another company (i.e., via Acquisition & Merger) it should be possible to quantum leap your market share while increasing the efficiencies of the operation by scale effects. In the above Example Chart our Hero has ca. 15% Customer Market Share and the Hero’s target ca. 10%. Thus after an acquisition our Hero would expect to get ca. 25% (if they play it well enough). Similarly we would expect a boost in profitability and hope for at least 38% if our Hero has 18% margin and our Target has 20%. Maybe even better as the scale should improve this further. Obviously, this kind of “math” assumes that our Hero and Target can work in isolation from the rest of the market and that no competitive forces would be at play to disrupt the well thought through plan (or that nothing otherwise disruptive happens in parallel with the merger of the two businesses). Of course such a venture comes with a price tag (i.e., the acquisition price) that needs to be factored into the overall economics of acquiring customers. As said most (Western) Operators are in a perpetual state of managing & controlling cost to maintain their Margin, protect and/or improve their EBITDA.
So one thing is theory! Let us see how the Dutch Mobile Markets Profitability Dynamics evolved over the 10 year period from 2003 to 2013;
From both KPN’s acquisition of Telfort as well as the acquisition & merger of Orange by T-Mobile above Margin vs. Subscriber Market Share Chart, we see that in general, the Market Share logic works. On the other hand the management of the integration of the business would have been fairly unlucky for that to be right. When it comes to the EBITDA logic it does look a little less obvious. KPN clearly got unlucky (if un-luck has something to do with it?) as their margin decline with a small uplift albeit still lower than where they started pre-acquisition. KPN should have expected a margin lift to 50+%. That did not happen to KPN – Telfort. T-Mobile did fare better although we do observe a margin uplift to around 30% that can be attributed to Opex synergies resulting from the integration of the two businesses. However, it has taken many Opex efficiency rounds to get the Margin up to 38% that was the original target for the T-Mobile – Orange transaction.
In the past it was customary to take lots of operators from many countries, plot their margin versus subscriber markets share, draw a straight line through the data points and conclude that the margin potential is directly related to the Subscriber Market Share. This idea is depicted by the Left Side Chart and the Straight line “Best” Fit to data.
Lets just terminate that idea … it is wrong and does not reflect the right margin dynamics as a function of the subscriber markets share. Furthermore, the margin dynamics is not a straight-line function of the subscriber market share but rather asymptotic falling off towards minus infinity, i.e., when the company have no subscribers and no revenue but non-zero cost. We also observed a diminishing return on additional market share in the sense that as more market share is gained smaller and smaller incremental margins are gained. The magenta dashed line in the Left Chart below illustrates how one should expect the Margin to behave as a function of Subscriber market share.
The Right Chart above shows has broken down the data points in country by country. It is obvious that different countries have different margin versus market share behavior and that drawing a curve through all of those might be a bit naïve.
So how can we understand this behavior? Let us start with making a very simple formula a lot more complex :–)
We can write the Marginas the ratio of Earning before Interest Tax Depreciation & Amortization (EBITDA)and Revenue R:, EBITDA is defined as Revenue minus Opex. Both Opex and Revenue I can de-compose into a fixed and a variable part: O = Of + AOPU x U and R = Rf + ARPU x U with AOPU being the Average Opex per User, ARPU the Average (blended) Revenue per User and U the number of users. For the moment I will be ignoring the fixed part of the revenue and write R = ARPU x U. Further, the number of users can be written as with being the market share and M being the market size. So we can now write the margin as
with and .
being valid for
The Margin is not a linear function of the Subscriber Market Share (if anybody would have expected that) but relates to the Inverse of Market Share.
Still the Margin becomes larger as the market share grows with maximum achievable margin of as the market share equals 1 (i.e., Monopoly). We observe that even in a Monopoly there is a limit to how profitable such a business can be. It should be noted that this is not a constant but a function of how operationally efficient a given operator is as well as its market conditions. Furthermore, as the market share reduces towards zero .
Fixed Opex (of) per total subscriber market: This cost element is in principle related to cost structure that is independent on the amount of customers that a given mobile operator have. For example a big country with a relative low population (or mobile penetration) will have higher fixed cost per total amount of subscribers than a smaller country with a larger population (or mobile penetration). Fixed cost is difficult to change as it depends on the network and be country specific in nature. For an individual Operator the fixed cost (per total market subscribers) will be influenced by;
Coverage strategy, i.e., to what extend the country’s surface area will be covered, network sharing, national roaming vs. rural coverage, leased bandwidth, etc..
Spectrum portfolio, i.e, lower frequencies are more economical than higher frequencies for surface area coverage but will in general have less bandwidth available (i.e., driving up the number of sites in capacity limited scenarios). Only real exception to bandwidth limitations of low frequency spectrum would be the APT700 band (though would “force” an operator to deploy LTE which might not be timed right given specifics of the market).
General economical trends, lease/rental cost, inflation, salary levels, etc..
Average Variable Opex per User (ou): This cost structure element capture cost that is directly related to the subscriber, such as
Any other variable cost directly associated with the customer (e.g., customer facing functions in the operator organization).
This behavior is exactly what we observe in the presented Margin vs. Subscriber Market Share data and also explains why the data needs to be treated on a country by country basis. It is worthwhile to note that after the higher the market share the less incremental margin gain should be expected for additional market share.
The above presented profitability framework can be used to test whether a given mobile operator is market & operationally efficient compared to its peers.
The overall Margin dynamics is shown above Chart for the various settings of fixed and variable Opex as well as a given operators ARPU. We see that as the fixed Opex (in relation to the total subscriber market) increasing it will get more difficult to get EBITDA positive and increasingly more market share is required to reach a reasonable profitability targets. The following maps a 3 player market according with the profitability logic derived here:
What we first notice is that operators in the initial phase of what you might define as the “Market-share Capture Phase” are extremely sensitive to setbacks. A small loss of subscriber market share (i.e. 2%) can tumble the operator back into the abyss (i.e, 15% Margin setback) and wreck havoc to the business model. The profitability logic also illustrates that once an operator has reached Market-share maturity adding new subscribers is less valuable than to keep them. Even big market share addition will only result in little additional profitability (i.e., the law of diminishing returns).
The derived Profitability framework can be used also to illustrate what happens to the Margin in a market-wise steady situation (i.e., only minor changes to an operators market share) or what the Market Share needs to be to keep a given Margin or how cost needs to be controlled in the event that ARPU drops and we want to keep our margin and cannot grow market share (or any other market, profitability or cost-structure exercise for that matter);
Above chart illustrates Margin as a function of ARPU & Cost (fixed & variable) Development at a fixed market share here chosen to be 33%. The starting point is an ARPU ru of EUR25.8 per month, a variable cost per user ou assumed to be EUR15 and a fixed cost per total mobile user market (of) of EUR0.5. The first scenario (a Orange Solid Line) with an end of period margin of 32.7% assumes that ARPU reduces with 2% per anno, that the variable cost can be controlled and likewise will reduce with 2% pa. Variable cost is here assumed to increase with 3% on an annual basis. During the 10 year period it is assumed that the Operators market share remains at 33%. The second scenario (b Red Dashed Line) is essential same as (a) with the only difference that the variable cost remains at the initial level of EUR15 and will not change over time. This scenario ends at 21.1% after 10 Years. In principle it shows that Mobile Operators will not have a choice on reducing their variable cost as ARPU declines (again the trade-off between certainty of cost and risk/uncertainty of revenue). In fact the most successful mature mobile operators are spending a lot of efforts to manage & control their cost to keep their margin even if ARPU & Revenues decline.
The above chart illustrates what market share is required to keep the margin at 36% when ARPU reduces with 2% pa, fixed cost increases with 3% pa and the variable cost either (a Orange Solid Line) can be reduced with 2% in line with the ARPU decline or (b Red Solid Line) remains fixed at the initial level. In scenario (a) the mobile operator would need to grow its market share to 52% to main its margin at 36%. This will obviously be very challenging as this would be on the expense of other operators in this market (here assume to be 3). Scenario (b) is extremely dramatic and in my opinion mission impossible as it requires a complete 100% market dominance.
Above Chart illustrates how we need to manage & control my variable cost compared to the –2% decline pa in order to keep the Margin constant at 36% assuming that the Operator Subscriber Market Share remains at 33% over the period. The Orange Solid Line in the Chart shows the –2% variable cost decline pa and the Red Dashed Line the variable cost requirement to keep the margin at 36%.
The following illustrates the Profitability Framework as described above applied to a few Western European Markets. As this only serves as an illustration I have chosen to show older data (i..e, 2006). It is however very easy to apply the methodology to any country and the BoAML Global Wireless Matrix with its richness in data can serve as an excellent source for such analysis. Needless to say the methodology can be extended to assess an operators profitability sensitivity to market share and market dynamics in general.
The Charts below shows the Equalized Market Share which simply means the fair market share of operators, i.e., if I have 3 operators the fair or equalized market share would 1/3 (33.3%), in case of 4 operators it should be 25% and so forth, I am also depicting what I call the Max Margin Potential this is simply the Margin potential at 100% Market Share at a given set of ARPU (ru), AOPU (ou) and Fixed Cost (of) Level in relation to the total market.
Netherlands Chart: Equalized Market Share assumes Orange has been consolidated with T-Mobile Netherlands. The analysis would indicate that no more than ca. 40% Margin should be expected in The Netherlands for any of the 4 Mobile Operators. Note that for T-Mobile and Orange small increases in market share should in theory lead to larger margins, while KPN’s margin would be pretty much un-affected by additional market share.
Germany Chart: Shows Vodafone to slightly higher and T-Mobile Deutschland slight lower in Margin than the idealized Margin versus Subscriber Market share. At the time T-Mobile had almost exclusive leased lines and outsourced their site infrastructure while Vodafone had almost exclusively Microwaves and owned its own site infrastructure. The two new comers to the German market (E-Plus and Telefonica-O2) is trailing on the left side of the Equalized Market Share. At this point in time should Telefonica and E-Plus have merged one would have expected them eventually (post-integration) to exceed a margin of 40%. Such a scenario would lead to an almost equilibrium market situation with remaining 3 operators having similar market shares and margins.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. I certainly have not always been very present during the analysis and writing.
Up-to 50% of Sites in Mobile Networks captures no more than 10% of Mobile Service Revenues.
The “Ugly” (cost) Tail of Cellular Networks can only be remedied by either removing sites (and thus low- or –no-profitable service) or by aggressive site sharing.
With Network Sharing expect up-to 35% saving on Technology Opex as well as future Opex avoidance.
The resulting Technology Opex savings easily translates into a Corporate Opex saving of up-to 5% as well as future Opex avoidance.
Active as well as Passive Network Sharing brings substantial Capex avoidance and improved sourcing economics by improved scale.
National Roaming can be an alternative to Network Sharing in low traffic and less attractive areas. Capex attractive but a likely Ebitda-pressure point over time.
“Sharing by Towerco” can be an alternative to Real Network Sharing. It is an attractive mean to Capex avoidance but is not Ebitda-friendly. Long-term commitments combined with Ebitda-risks makes it a strategy that should to be considered very carefully.
Network Sharing frees up cash to be spend in other areas (e.g., customer acquisition).
Network Sharing structured correctly can result in faster network deployment –> substantial time to market gains.
Network Sharing provides substantially better network quality and capacity for a lot less cash (compared to standalone).
Instant cell split option easy to realize by Network Sharing –> cost-efficient provision of network capacity.
Network Sharing offers enhanced customer experience by improved coverage at less economics.
Network Sharing can bring spectral efficiency gains of 10% or higher.
The purpose of this story is to provide decision makers, analysts and general public with some simple rules that will allow them to understand Network Sharing and assess whether it is likely to be worthwhile to implement and of course successful in delivering the promise of higher financial and operational efficiency.
Today’s Technology supports almost any network sharing scenario that can be thought of (or not). Financially & not to forget Strategically this is far from so obvious.
Network Sharing is not only about Gains, its evil twin Loss is always present.
Network Sharing is a great pre-cursor to consolidation.
Network sharing has been the new and old black for many years. It is a fashion that that seems to stay and grow with and within the telecommunications industry. Not surprising as we shall see that one of the biggest financial efficiency levers are in the Technology Cost Structure. Technology wise there is no real stumbling blocks for even very aggressive network sharing maximizing the amount of system resources being shared, passive as well as active. The huge quantum-leap in availability of very high quality and affordable fiber optic connectivity in most mature markets, as well between many countries, have pushed the sharing boundaries into Core Network, Service Platforms and easily reaching into Billing & Policy Platforms with regulatory and law being the biggest blocking factor of Network-as-a-Service offerings. Below figure provides the anatomy of network sharing. It should of course be noted that also within each category several flavors of sharing is possible pending operator taste and regulatory possibilities.
Network Sharing comes in many different flavors. To only consider one sharing model is foolish and likely will result in wrong benefit assessment. Setting a sharing deal up for failure down the road (if it ever gets started). It is particular important to understand that while active sharing provides the most comprehensive synergy potential, it tends to be a poor strategy in areas of high traffic potential. Passive sharing is a much more straightforward strategy in such areas. In rural areas, where traffic is less of an issue and profitability is a huge challenge, aggressive active sharing is much more interesting. One should even consider frequency sharing if permitted by regulatory authority. The way I tend to look at the Network Sharing Flavors are (as also depicted in the Figure below);
Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
Coverage Limited Areas (i.e., some urban environments, mainly sub-urban and rural) – Minimum Passive Sharing should be pursued with RAN (Active) Sharing providing an additional economical advantage.
Rural Areas – National Roaming or Full RAN sharing including frequency sharing (if regulatory permissible).
One of the first network sharing deals I got involved in was back in mid-2001 in The Netherlands. This was at the time of the Mobile Industry’s first real cash crises. Just as we were about to launch this new exiting mobile standard (i.e., UMTS) that would bring Internet to the pockets of the masses. After having spend billions & billions of dollars (i.e., way too much of course) on high-frequency 2100MHz UMTS spectrum, all justified by an incredible optimistic (i.e., said in hindsight!) belief in the mobile internet business case, the industry could not afford to deploy the networks required to make our wishful thinking come true.
T-Mobile (i.e., aka Ben BV) engaged with Orange (i.e., aka Dutchtone) in The Netherlands on what should have been a textbook example of the perfect network sharing arrangement. We made a great business case for a comprehensive network sharing. It made good financial and operational sense at the setup. At the time the sharing game was about Capex avoidance and trying to get the UMTS network rolled out as quickly as possible within very tight budgets imposed by our mother companies (i.e., Deutsche Telekom and France Telecom respectively). Two years down the road we revised our strategic thoughts on network sharing. We made another business case for why deploying on standalone made more sense than sharing. At that time the only thing T-we (Mobile NL) really could agree with Orange NL about was ancillary cabinet sharing and of course the underlying site sharing. Except for agreeing not to like the Joint Venture we created (i.e., RANN BV), all else were at odds, e.g., supplier strategy, degree of sharing, network vision, deployment pace, etc… Our respective deployment strategies had diverged so substantially from each other that sharing no longer was an option. Further, T-Mobile decided to rely on the ancillary cabinet we had in place for GSM –> so also no ancillary sharing. This was also at a time where cabinets and equipment took up a lot of space (i.e., do you still remember the first & 2nd generation 3G cabinets?). Many site locations simply could not sustain 2 GSM and 2 UMTS solutions. Our site demand went through the roof and pretty much killed the sharing case.
Starting point: Site Sharing, Shared Built, Active RAN and transport sharing.
Just before breakup I: Site Sharing, cabinet sharing if required, shared built where deployment plans overlapped.
Just before breakup II:Crisis over and almost out. Cash and Capex was no longer as critical as it was at startup.
It did not help that the Joint Venture RANN BV created to realize T-Mobile & Orange NL shared UMTS network plans frequently were at odds with both founding companies. Both entities still had their full engineering & planning departments including rollout departments (i.e., in effect we tried to coordinate across 3 rollout departments & 3 planning departments, 1 from T-Mobile, 1 from Orange and 1 from RANN BV … pretty silly! Right!). Eventually RANN BV was dissolved. The rest is history. Later T-Mobile NL acquired Orange NL and engaged in a very successful network consolidation (within time and money).
The economical benefits of Sharing and Network Consolidation are pretty similar and follows pretty much the same recipe.
Luckily (if Luck has anything to do with it?) since then there have been more successful sharing projects although the verdict is still out whether these constructs are long-lived or not and maybe also by what definition success is measured.
Judging from the more than 34 Thousand views on my various public network sharing presentations, I have delivered around the world since 2008, there certainly seem to be a strong and persistent interest in the topic.
I have worked on Network Sharing and Cost Structure Engineering since the early days of 2001. Very initially focus was on UMTS deployments, the need and requirements to deploy much more cash efficient. Cash was a very scarce resource after the dot-com crash between 2000 & 2003. After 2004 the game changed to be an Opex Saving & Avoidance game to mitigate stagnating customer growth and revenue growth slow down.
I have in detail studied many Network Sharing strategies, concepts and deals. A few have turned out successful (at least still alive & kicking) and many more un-successful (never made it beyond talk and analysis). One of the most substantial Network Sharing deals (arguable closer to network consolidation), I work on several years ago is still very much alive and kicking. That particular setup has been heralded as successful and a poster-boy example of the best of Network Sharing (or consolidation). However, by 2014 there has hardly been any sites taken out of operation (certainly no where close to the numbers we assumed and based our synergy savings on).
More than 50% of all network related TCO comes from site-related operational and capital expenses.
Despite the great economical promises and operational efficiencies that can be gained by two mobile operations (fixed for that matter as well) agreeing to share their networks, it is important to note that
It is NOT enough to have a great network sharing plan. A very high degree of discipline and razor-sharp focus in project execution is crucial for delivering network sharing within money and time.
With introduction of UMTS & Mobile Broadband the mobile operator’s margin & cash have come under increasing pressure (not helped by voice revenue decline & saturated markets).
Technology addresses up-to 25% of a Mobile Operators Total Opex & more than 90% of the Capital Expenses.
Radio Access Networks accounts easily for more than 50% of all Network Opex and Capex.
For a reasonable efficient Telco Operation, Technology Cost is the most important lever to slow the business decline, improve financial results and return on investments.
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 suchTower 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
Little restructuring & termination cost expected.
High Capex avoidance potential.
High Opex avoidance potential.
Little to no infrastructure write-offs.
Little to no risk of contract termination penalties.
“Normal” network deployment project (though can be messed up by too many cooks syndrome).
Best network potential.
(2) The Steady State Phase, where a substantial part of the networks have been rollout out, tend to be the most complex and costly phase to engage in Network Sharing passive and of course active sharing. A substantial amount of site leases would need to be broken, terminated or re-structured to allow for network sharing. In all cases either penalties or lease increases are likely to result. Infrastructure supplier contracts, typically maintenance & operations agreements, might likewise be terminated or changed substantially. Same holds for leased transmission. Write-off can be very substantial in this phase as relative new sites might be terminated, new radio equipment might become redundant or phased-out, etc If one or both sharing partners are in this phase of the business & network cycle the chance of a network sharing agreement is low. However, if a substantial amount of both parties site locations will be used to enhance the resulting network and a substantial part of the active equipment will be re-used and contracts expanded then sharing tends to be going ahead. A good example of this is in the UK with Vodafone and O2 site sharing agreement with the aim to leapfrog number of sites to match that of EE (Orange + T-Mobile UK JV) for improved customer experience and remain competitive with the EE network.
Network Sharing in the Steady State Phase is characterized by
Very high restructuring & termination cost expected.
None or little Capex synergies.
Substantial Opex savings potential.
Very high infrastructure write-offs.
Very high termination penalties incl. site lease termination.
Highly complex consolidation project.
Medium to long-term network quality & optimization issues.
(3) Once operators approaches the Modernization Phase more aggressive network sharing scenarios can be considered as the including joint sourcing and infrastructure procurement (e.g., a la T-Mobile UK and Hutchinson in UK). At this stage typically the remainder of the site leases term will be lower and penalties due to lease termination as a result lower as well. Furthermore, at this point in time little (or at least substantially lower than in the steady state phase) residual value should remain in the active and also passive infrastructure. The Modernization Phase is a very opportune moment to consider network sharing, passive as well as active, resulting in both substantial Capex avoidance and of course very attractive Opex savings mitigating a stagnating or declining topline as well as de-risking future loss of profitability.
Network Sharing in the Modernization Phase is characterized by
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.