Archive for category Broadband Telecoms

5G Economics – The Numbers (Appendix X).

5G essense


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;

  1. Network-augmented driving and support for varous degrees of autonomous driving would require all roads to be covered (however small).
  2. 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.
  3. Given many users personal area IoT networks (e.g., fitness & health monitors, location detection, smart-devices in general) ubiquitous becomes essential.
  4. 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);

broadband coverage in eu28

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).

delta to 100% hh coverage

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.


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;


Source: 5G Economics – An Introduction (Chapter 1).

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;

broadband speed hh coverage.png

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);

opensignal state of lte 2017

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.

Let us look at cellular spectral efficiency to be expected from 5G. Using the well known framework;

cellular capacity fundamentals

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.

In the following I rely largely on Jonathan Rodriquez great book on “Fundamentals of 5G Mobile Networks” as a source of inspiration.

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.

most traffic in very few cells

Source: Slideshare presentation by Dr. Kim “Capacity planning in mobile data networks experiencing exponential growth in demand.”

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.


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.

I am not going to address this topic too much here. It has to a great extend been addressed almost to ad nauseam in 5G Economics – An Introduction (Chapter 1) and 5G Economics – The Tactile Internet (Chapter 2). I think this particular aspect of 5G is being over-hyped in comparison to how important it ultimately will turn out to be from a return on investment perspective.

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.


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).

A consensus number for 2020 appears to be 30±5 Billion IoT devices with lower numbers based on 2015 forecasts and higher numbers typically from 2016.

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.

However, 18,000 per km2 in urban areas appears somewhat of a challenge. Getting to 1 Million per km2 … hmmm … we will see around 2035 to 2040 (I have added an internet reminder for a check-in by 2035).

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;

iot connections 2024

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.


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.


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.

From a recent 2016 survey from Ask Your Target Market on smartphone consumers requirements to battery lifetime and charging times;

  • 64% of smartphone owners said they are at least somewhat satisfied with their phone’s battery life.
  • 92% of smartphone owners said they consider battery life to be an important factor when considering a new smartphone purchase.
  • 66% said they would even pay a bit more for a cell phone that has a longer battery life.

Looking at the mobile smartphone & tablet non-voice consumption it is also clear why battery lifetime and not in-important the charging time matters;

smartphone usage time per day

Source: eMarketer, April 2016. While 2016 and 2017 are eMarketer forecasts (why dotted line and red circle!) these do appear well in line with other more recent measurements.

Non-voice smartphone & tablet based usage is expected by now to exceed 4 hours (240 minutes) per day on average for US Adults.

That longer battery life-times are needed among smartphone consumers is clear from sales figures and anticipated sales growth of smartphone power-banks (or battery chargers) boosting the life-time with several more hours.

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.


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.


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5G Economics – The Tactile Internet (Chapter 2)

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.

elephant in the room

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).

tactile internet 1

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.

tactile internet 2

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;

latency in networks

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).

All true, if you limited the tele-robot and patient to a distance of no more than 50 km (and likely less!) from the remote medical doctor. In this setup and definition of the Tactile Internet, having a top eye surgeon placed in Delhi would not be able to operate child (near blindness) in a remote village in Madhya Pradesh (India) approx. 800+ km away. Note India has the largest blind population in the world (also by proportion) with 75% of cases avoidable by medical intervention. At best, these specifications allow the doctor not to be in the same room with the patient.

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 “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.human senses

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.

We have, based on vision, the surgeon controlling a remote surgical robot stating that anything below 175 ms latency is imperceptible. There is research showing that haptic feedback delay below 30 ms appears to be un-detectable.

John Carmack, CTO of Oculus VR Inc, based on in particular vision (in a fairly dynamic environment) that  “.. when absolute delays are below approximately 20 milliseconds they are generally imperceptible.” particular as it relates to 3D systems and VR/AR user experience which is a lot more dynamic than watching content loading. Moreover, according to some recent user experience research specific to website response time indicates that anything below 100 ms wil be perceived as instantaneous. At 1 second users will sense the delay but would be perceived as seamless. If a web page loads in more than 2 seconds user satisfaction levels drops dramatically and a user would typically bounce.

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).

reaction times

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.

latency budget

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;

local latency should be considered

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.


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).

One of my absolute current favorite robotics use case that comes somewhat close to a 5G Tactile Internet vision, done with 4G technology, is the example of Ocado’s warehouse automation in UK. Ocado is the world’s largest online-only grocery retailer with ca. 50 thousand lines of goods, delivering more than 200,000 orders a week to customers around the United Kingdom. The 4G network build (by Cambridge Consultants) to support Ocado’s automation is based on LTE at unlicensed 5GHz band allowing Ocado to control 1,000 robots per base station. Each robot communicates with the Base Station and backend control systems every 100 ms on average as they traverses ca. 30 km journey across the warehouse 1,250 square meters. A total of 20 LTE base stations each with an effective range of 4 – 6 meters cover the warehouse area. The LTE technology was essential in order to bring latency down to an acceptable level by fine tuning LTE to perform under its lowest possible latency (<10 ms).

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.


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

  • Latency.
  • Jitter (i.e., variation of latency).
  • Connection reliability.
  • Network congestion.
  • Robot-2-Robot communications.
  • Robot-2-ROS (i.e., general robotics operations system).
  • Computing architecture: distributed, centralized, elastic computing, etc…
  • System stability.
  • Range.
  • Power budget (e.g., power limitations, re-charging).
  • Redundancy.
  • Sensor & actuator fusion (e.g., consolidate & align data from distributed sources for example sensor-actuator network).
  • Context.
  • Autonomy vs human control.
  • Machine learning / machine intelligence.
  • Safety (e.g., human and non-human).
  • Security (e.g., against cyber threats).
  • User Interface.
  • System Architecture.
  • etc…

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;

networked robotics


I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.


  1. “NGMN 5G White Paper” by R.El Hattachi & J. Erfanian (NGMN Alliance, February 2015).
  2. “The Tactile Internet” by ITU-T (August 2014). Note: in this Blog this paper is also referred to as the Tactile Internet Vision.
  3. “5G: Personal Mobile Internet beyond What Cellular Did to Telephony” by G. Fettweis & S. Alamouti, (Communications Magazine, IEEE , vol. 52, no. 2, pp. 140-145, February 2014).
  4. “The Tactile Internet: Vision, Recent Progress, and Open Challenges” by Martin Maier, Mahfuzulhoq Chowdhury, Bhaskar Prasad Rimal, and Dung Pham Van (IEEE Communications Magazine, May 2016).
  5. “John Carmack’s delivers some home truths on latency” by John Carmack, CTO Oculus VR.
  6. “All About Robotic Surgery” by The Official Medical Robotics News Center.
  7. “The surgeon who operates from 400km away” by BBC Future (2014).
  8. “The Case for VM-Based Cloudlets in Mobile Computing” by Mahadev Satyanarayanan et al. (Pervasive Computing 2009).
  9. “Perception of Delay in Haptic Telepresence Systems” by Markus Rank et al. (pp 389, Presence: Vol. 19, Number 5).
  10. “Neuroscience Exploring the Brain” by Mark F. Bear et al. (Fourth Edition, 2016 Wolters Kluwer).
  11. “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.
  12. “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.
  13. “Vision How it works and what can go wrong” by John E. Dowling et al. (2016, The MIT Press).
  14. “Visual Psychophysics From Laboratory to Theory” by Yhong-Lin Lu and Barbera Dosher (2014, MIT Press).
  15. “The Time Delay in Human Vision” by D.A. Wardle (The Physics Teacher, Vol. 36, Oct. 1998).
  16. “What do we perceive in a glance of a real-world scene?” by Li Fei-Fei et al. (Journal of Vision (2007) 7(1); 10, 1-29).
  17. “Detecting meaning in RSVP at 13 ms per picture” by Mary C. Potter et al. (Attention, Perception, & Psychophysics, 76(2): 270–279).
  18. “Banana or fruit? Detection and recognition across categorical levels in RSVP” by Mary C. Potter & Carl Erick Hagmann (Psychonomic Bulletin & Review, 22(2), 578-585.).
  19. “Human time perception and its illusions” by David M. Eaglerman (Current Opinion in Neurobiology, Volume 18, Issue 2, Pages 131-136).
  20. “How Much Faster is Fast Enough? User Perception of Latency & Latency Improvements in Direct and Indirect Touch” by J. Deber, R. Jota, C. Forlines and D. Wigdor (CHI 2015, April 18 – 23, 2015, Seoul, Republic of Korea).
  21. “Response time in games: Requirements and improvements” by Kjetil Raaen (Ph.D., 2016, Department of Informatics, The Faculty of Mathematics and Natural Sciences, University of Oslo).
  22. “Latency and player actions in online games” by Mark Claypool & Kajal Claypool (Nov. 2006, Vol. 49, No. 11 Communications of the ACM).
  23. “The Brewing Storm in Cloud Gaming: A Measurement Study on Cloud to End-User Latency” by Sharon Choy et al. (2012, 11th Annual Workshop on Network and Systems Support for Games (NetGames), 1–6).
  24. “On the impact of delay on real-time multiplayer games” by Lothar Pantel and Lars C. Wolf (Proceedings of the 12th International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV ’02, New York, NY, USA, pp. 23–29. ACM.).
  25. “Oculus Rift’s time warping feature will make VR easier on your stomach” from ExtremeTech Grant Brunner on Oculus Rift Timewarping. Pretty good video included on the subject.
  26. “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.
  27. “Ocado: next-generation warehouse automation” by Cambridge Consultants.
  28. “Ocado has a plan to replace humans with robots” by Business Insider UK (May 2015). Note that Ocado has filed more than 73 different patent applications across 32 distinct innovations.
  29. “The Robotic Grocery Store of the Future Is Here” by MIT Technology Review (December 201
  30. “Cloud Robotics: Architecture, Challenges and Applications.” by Guoqiang Hu et al (IEEE Network, May/June 2012).

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5G Economics – An Introduction (Chapter 1)

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.


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.

capacity fundamentals 

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 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.

How much faster is fast enough from a customer experience perspective? According with John Carmack, CTO of Oculus Rift, “.. when absolute delays are below approximately 20 milliseconds they are generally imperceptible.” particular as it relates to 3D systems and VR/AR user experience which is a lot more dynamic than watching content loading. According to recent research specific to website response time indicates that anything below 100 ms wil be perceived as instantaneous. At 1 second users will sense the delay but would be perceived as seamless. If a web page loads in more than 2 seconds user satisfaction levels drops dramatically and a user would typically bounce. Please do note that most of this response or download time overhead has very little to do with connection throughput, but to do with a host of other design and configuration issues. Cranking up the bandwidth will not per se solve poor browsing performance.

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.     

what we are waiting for


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..).

so …

who cares

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.
  • etc…

Recently William Webb published a book titled “The 5G Myth: And why consistent connectivity is a better future” (reminder: a myth is a belief or set of beliefs, often unproven or false, that have accrued around a person, phenomenon, or institution). William Web argues;

  • 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).

Rest assured, over the next 10 to 15 years we will see whether William Webb will end up in the same category as other very smart in the know people getting their technology predictions proven wrong (e.g., IBM Chairman Thomas Watson’s famous 1943 quote that “… there is a world market for maybe five computers.” and NO! despite claims of the contrary Bill Gates never said “640K of memory should be enough for anybody.”).

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;

5G cost impact

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?

At this moment very few 5G market uptake forecasts have yet made it out in the open. However, taking the Strategy Analytics August 2016 5G FC of ca. 690 million global 5G connections by year 2025 we can get an impression of how 5G uptake might look like;

mobile uptake projections

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?


Let’s start with the 5G technology vision as being presented by NGMN and GSMA.

GSMA (Groupe Speciale Mobile Association) 2014 paper entitled ‘Understanding 5G: Perspective on future technology advancements in mobile’ have identified 8 main requirements; 

1.    1 to 10 Gbps actual speed per connection at a max. of 10 millisecond E2E latency.

Note 1: This is foreseen in the NGMN whitepaper only to be supported in dense urban areas including indoor environments.

Note 2: Throughput figures are as experienced by the user in at least 95% of locations for 95% of the time.

Note 3: In 1 ms speed the of light travels ca. 200 km in optical fiber.

2.    A Minimum of 50 Mbps per connection everywhere.

Note 1: this should be consistent user experience outdoor as well as indoor across a given cell including at the cell edge.

Note 2: Another sub-target under this promise was ultra-low cost Networks where throughput might be as low as 10 Mbps.

3.    1,000 x bandwidth per unit area.

Note: notice the term per unit area & think mm-wave frequencies; very small cells, & 100s of MHz frequency bandwidth. This goal is not challenging in my opinion.

4.    1 millisecond E2E round trip delay (tactile internet).

Note: The “NGMN 5G White Paper” does have most 5G use cases at 10 ms allowing for some slack for air-interface latency and reasonable distanced transport to core and/or aggregation points.

5.    Massive device scale with 10 – 100 x number of today’s connected devices.

Note: Actually, if one believes in the 1 Million Internet of Things connections per km2 target this should be aimed close to 1,000+ x rather than the 100 x for an urban cell site comparison.

6.    Perception of 99.999% service availability.

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 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.

technology cost pre-5G

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.


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.


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,

  • Ignoring that such systems originally were designed and works best for TDD based systems.
  • 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.

The observant reader will know that I have largely ignored the following challenges of massive MiMo (see also Larsson et al’s “Massive MiMo for Next Generation Wireless Systems” 2014 paper);

  1. mMiMo designed for TDD, but works at some performance penalty for FDD.
  2. 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!).
  3. mMiMo performance leap frog comes at the price of high computational complexity (e.g., should be factored into the deployment cost).
  4. 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.


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.


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);

lte performance 2016

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 fixed peak 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).

video resolution vs bandwitdh requirements

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;

viewing distance vs display size

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.

urban lte latency 2016

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;

urban lte dl speed

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.

LTE household coverage

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.



  1. “NGMN 5G White Paper” by R.El Hattachi & J. Erfanian (NGMN Alliance, February 2015).
  2. “Understanding 5G: Perspectives on future technological advancement in mobile” by D. Warran & C. Dewar (GSMA Intelligence December 2014).
  3. “Fundamentals of 5G Mobile Networks” by J. Rodriguez (Wiley 2015).
  4.  “The 5G Myth: And why consistent connectivity is a better future” by William Webb (2016).
  5. “Software Networks: Virtualization, SDN, 5G and Security”by G. Pujolle (Wile 2015).
  6. “Large MiMo Systems” by A. Chockalingam & B. Sundar Rajan (Cambridge University Press 2014).
  7. “Millimeter Wave Wireless Communications” by T.S. Rappaport, R.W. Heath Jr., R.C. Daniels, J.N. Murdock (Prentis Hall 2015).
  8. “The Limits of Human Vision” by Michael F. Deering (Sun Microsystems).
  9. “Quad HD vs 1080p vs 720p comparison: here’s what’s the difference” by Victor H. (May 2014).
  10. “Broadband Coverage in Europe 2015: Mapping progress towards the coverage objectives of the Digital Agenda” by European Commission, DG Communications Networks, Content and Technology (2016).

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

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

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

“I would NOT give up Internet for life for a Million+ US Dollars … oh … BUT… I don’t want to pay more than a couple of bucks for it either” (actually for a mature postpaid-rich market the chances are that over your expected life-time you will pay between 30 to 40 thousand US$ for mobile internet & voice & some messaging).

Price plans are fascinating! … Particular the recent data-centric price plans bundling in legacy services such as voice and SMS.

Needles to say that a consumer today often needs an advanced degree in science to really understand the price plans they are being presented. A high degree of trust is involved in choosing a given plan. The consumer usually takes what has been recommended by the shop expert (who most likely doesn’t have an advanced science degree either). This shop expert furthermore might (or might not) get a commission (i.e., a bonus) selling you a particular plan and thus in such a case hardly is the poster child of objectiveness.

How does the pricing experts come to the prices that they offer to the consumer? Are those plans internally consistent … or  maybe not?

It becomes particular interesting to study data-centric price plans that try to re-balance Mobile Voice and SMS.

How is 4G (i.e., in Europe also called LTE) being charged versus “normal” data offerings in the market? Do the mobile consumer pay more for Quality? Or maybe less?

What is the real price of mobile data? … Clearly, it is not the price we pay for a data-centric price plan.

A Data-centric Tale of a Country called United & a Telecom Company called Anything Anywhere!

As an example of mobile data pricing and in particular of data-centric mobile pricing with Voice and SMS included, I looked at a Western European Market (let’s call it United) and a mobile operator called Anything Anywhere. Anything Anywhere (AA) is known for its comprehensive & leading-edge 4G network as well as several innovative product ideas around mobile broadband data.

In my chosen Western European country United, voice revenues have rapidly declined over the last 5 years. Between 2009 to 2014 mobile voice revenues lost more than 36% compared to an overall revenue loss of “only” 14%. This corresponds to a compounded annual growth rate of minus 6.3% over the period. For an in depth analysis of the incredible mobile voice revenue losses the mobile industry have incurred in recent years see my blog “The unbearable lightness of mobile voice”.

Did this market experience a massive uptake in prepaid customers? No! Not at all … The prepaid share of the customer base went from ca. 60% in 2009 to ca. 45% in 2014. So in other words the Postpaid base over the period had grown with 15% and in 2014 was around 55%. This should usually have been a cause for great joy and incredible boost in revenues. United is also a market that has largely managed not to capitalize economically on substantial market consolidation.

As it is with many other mobile markets, engaging & embracing the mobile broadband data journey has been followed by a sharp decline in the overall share of voice revenue from ca. 70% in 2009 to ca. 50% in 2014. An ugly trend when the total mobile revenue declines as well.

The Smartphone penetration in United as of Q1 2014 was ca. 71% with 32% iOS-based devices. Compare this to 2009 where the smartphone penetration was ca. 21% with iOS making out around 75+%.

Our Mobile Operator AA has the following price plan structure (note: all information is taken directly from AA’s web site and can be found back if you guess which company it applies to);

  • Data-centric price plans with unlimited Voice and SMS.
  • Differentiated speed plans, i.e., 4G (average speed advertised to 12 – 15 Mbps) vs. Double Speed 4G (average speed advertised to 24 – 30 Mbps).
  • Offer plans that apply Europe Union-wide.
  • Option to pay less for handsets upfront but more per month (i.e., particular attractive for expensive handsets such as iPhone or Samsung Galaxy top-range models).
  • Default offering is 24 month although a shorter period is possible as well.
  • Offer SIM-only data-centric with unlimited voice & SMS.
  • Offer Data-only SIM-only plans.
  • Further you will get access to extensive “WiFi Underground”. Are allowed tethering and VoIP including Voice-calling over WiFi.

So here is an example of AA’s data-centric pricing for various data allowances. In this illustration I have chosen to add an iPhone 6 Plus (why? well I do love that phone as it largely replaces my iPad outside my home!) with 128GB storage. This choice have no impact on the fixed and variable parts of the respective price plans. For SIM-Only plans in the data below, I have added the (Apple) retail price of the iPhone 6 Plus (light grey bars). This is to make the comparison somewhat more comparable. It should of course be clear that in the SIM-only plans, the consumer is not obliged to buy a new device.

tco 24 month

  • 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.

price per giga byte

  • 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;

{P_{Tot}} = {P_{Fixed}} + {P_{Data}}({U_{GB}}) = {P_{Fixed}} + {p_{GB}}U_{GB}^\beta

As will be described in more detail below, the data-centric price {P_{Tot}} is structured in what can be characterized as a “Fixed Service Fee”  {P_{Fixed}} and a variable “Data Consumption Price{P_{Data}} that depends on a given price-plan’s data allowance {U_{GB}} (i.e., GB is Giga Byte). The “Data Consumption Price{P_{Data}} is variable in nature and while it might be a complex (i.e. in terms of complexity) function of data allowance {U_{GB}} it typically be of the form {p_{GB}}U_{GB}^\beta with the exponent \beta (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 \beta 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 \beta 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 {U_{GB}} in a Log-Log format would result in a fairly straight-line.

examples of power-law behaviour

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;

\frac{{{P_{Tot}}}}{{{U_{GB}}}} = \frac{{{P_{Fixed}} + {p_{GB}}U_{GB}^\beta }}{{{U_{GB}}}}  = \limits^{\beta  = 1} {p_{GB}} + {P_{Fixed}}U_{GB}^{ - 1}

For very large data allowances {U_{GB}} the price-centric per GB would asymptotically converge to {p_{GB}}, i.e., the unit cost of a GB. As {p_{GB}} is usually a lot smaller than {P_{Fixed}}, we see that there is another limit, where the allowance {U_{GB}} 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 {p_{GB}} is (a lot) smaller than {P_{Fixed}}U_{GB}^{ - 1} , 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.

data centric price dynamics

  • 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.

data price analysis

  • 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;

fixed part of data-centric pricing

  • 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;


4G Speed

Price per GB

Pay Less Upfront More per Month



Pay Upfront & Less per Month






SIM-Only Data Only


2 (only 2 data points)

variable data price analysis

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;

standard pricing philosophy in mobile domain

  • 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!

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