pain points in a cellular data network · carrier’s 3g/4g network. igr focuses on this portion of...

and

present:

Pain Points in a Cellular Data Network

Whitepaper

Published: Second Quarter, 2012 Version 1.0

iGR Inc. 12400 W. Hwy 71 Suite 350 PMB 341 Austin TX 78738

Distribution of this report outside of your company or organization is strictly prohibited. Copyright © 2012 iGillottResearch Inc. 1

Table of Contents

Executive Summary..................................................................................................... 1

How Much Bandwidth? ............................................................................................... 2 Who’s Using the Bandwidth? ..............................................................................................2

Forecasted Mobile Bandwidth Consumption .......................................................................... 3 Table 1: U.S. Bandwidth Demand by User Category, 2011-2016 (MB/month) ....................... 3 Figure 1: U.S. Mobile Bandwidth Demand (MB/month), 2011-2016 ...................................... 4

When Is Bandwidth Used? .......................................................................................... 5 Figure 2: Change in Percent Usage per Hour, 2011 vs. 2016 .................................................. 5 Figure 3: Mobile Bandwidth Use by Time of Day (GB/hour/POP) ........................................... 6 When Bandwidth Demand Exceeds Average Network Capability .......................................... 7

Where Pain Points Might Occur ................................................................................... 8 Table 2: Mobile Data per Day (GB/day/KM2) .......................................................................... 8 Figure 4: Bandwidth Demand on a KM2 Basis (GB/hour/KM2 over average bandwidth demand) .................................................................................................................................. 9

Factoring in Population Movement ........................................................................... 10 Figure 5: Theoretical U.S. City ............................................................................................... 10 Figure 6: Theoretical Bandwidth Pain Points in City X .......................................................... 11

Methodology ............................................................................................................ 13 iGR End User Quantitative and Qualitative Studies ............................................................ 13

About iGR ................................................................................................................. 14 Disclaimer ........................................................................................................................ 14

This research is provided as a member benefit for the exclusive use of members of PCIA – The Wireless Infrastructure Association. It is made available by a partnership between PCIA and iGR.


Executive Summary

It is no secret that mobile/cellular data usage will greatly increase over the next few years, driven by ongoing LTE rollouts and the increasing adoption of smartphones, tablets and other data devices. iGR’s own forecasts suggest that by 2016 U.S. end users will devour about 2.6 GB of cellular data per month (on average).

What’s not typically discussed is the impact this data consumption will have when it is condensed into relatively small geographic areas – like the downtown business district of a city. Put simply, people move around and they use devices and data. And, they use them a bit more every day. As devices and networks improve, the easier it becomes for people to rely less on the local storage of content (music, video, etc.) and more on accessing what they want from the “cloud.”

The model presented in this report forecasts the severity of the problem that mobile operators face. In a hypothetical City X – 3 million people living in a metro area of 500 KM2 – our model suggests than in 2011 there is approximately 4.52 GB/day/KM2 in mobile bandwidth demand that the cellular data network cannot meet. By 2016, our model suggests that by 2016, this demand will rise to 72 GB/day/KM2.

If iGR further assumes that, on average, 50 percent of the people in City X commute to work and congregate in an area of, perhaps, 20 KM2 (to represent the core business district) then the mobile bandwidth demand in that 20 KM2 will increase by a factor of 12.5 over the average for the entire city. In other words, the unmet bandwidth demand problem in 2016 grows from 72 GB/day/KM2 on average to 900 GB/day/KM2 in that core 20 KM2 area – because people move about and congregate in a comparatively small area. For the sake of convenience, iGR refers to these areas as cellular network “pain points.”

Although new network technologies, such as LTE or LTE Advanced combined with innovative antenna and network design may be able to handle the average level of traffic, iGR’s research suggests that these “pain-points” will necessitate a different approach to network architecture. In short, the heterogeneous network (or het-net).

This whitepaper provides an overview of iGR’s localized bandwidth model and discusses the hypothetical impact of “pain points” on a cellular carrier’s 3G / 4G network.


How Much Bandwidth?

iGR’s mobile broadband bandwidth forecast assumes that the introduction of new cellular network technology (HSPA+, LTE) will increase peoples’ consumption of wireless bandwidth. In short, if you give someone a 1 Mbps “pipe,” they’ll use it. And, with lower latency, network consumption also increases. Applications themselves also tend to take greater advantage of bigger “pipes” by transmitting more data than they might through lower capacity connections.

iGR’s mobile bandwidth forecast also assumes that Internet-based services – audio-/video-streaming, social networking, video calling, etc. – will increase the amount of cellular bandwidth consumed. Similarly, the growing adoption of smartphones and tablets in the U.S. further drives wireless bandwidth consumption.

The increasing power of mobile devices (single core, to dual core to quad-core in 2012) also drives wireless bandwidth consumption. Moreover, most mobile devices are accessing the “normal” Internet – the same Internet that people view from their laptops/desktops. The full HTML browsing capabilities of mobile devices increases the amount of bandwidth people consume over 3G/4G networks. Many mobile apps regularly poll for updates, as well, which also drives up bandwidth consumption.

Who’s Using the Bandwidth?

In its mobile bandwidth forecast, iGR created several categories of users: light, medium, heavy, and extreme, and developed bandwidth estimates per category of user based on survey data and other sources. iGR then weighted connections/subscribers across those categories – e.g., iGR assumed that there are more light and medium users than heavy and extreme users.

The following describes some of the characteristics of each of iGR’s connection categories:

o Light Connection: Casual, infrequent data use; a minimal amount of web browsing, social networking, photo sending, email, mapping, etc.

o Medium Connection: Less casual, more frequent data use than a Light connection; perhaps includes the addition of some usage of audio/video streaming and application downloading.

o Heavy Connection: Significant and frequent use of the mobile device and a variety of applications – audio and video streaming, application downloads, social networking, email, etc. This type of connection might represent a mobile worker who travels several days per week.


o Extreme Connection: These are connections that look a great deal like a wired Internet connection – i.e., several gigabytes (GB) of usage per month. This type of connection is likely to be a laptop/tablet tethered to a smartphone or a connection via a USB/embedded modem. The person behind the device(s) might be a mobile worker who’s always out of the office or a consumer running a BitTorrent client, constantly checking Facebook, or downloading music or podcasts.

Additionally, iGR’s forecast grows connections/subscribers over the forecast period and bandwidth consumed per connection over the forecast period. Finally, the forecast assumes a change in the distribution of connections by usage category. For example, over time iGR expects to see Light users become Medium users and Medium users become Heavy users.

A connection typically corresponds to a device and connections can exceed subscribers. For example, a mobile worker in North America might have three devices – a smartphone, laptop and a tablet. A consumer might have two (a smartphone and a tablet).

These devices will not always connect over a 3G/4G network; WiFi might instead be used. However, iGR’s model estimates the bandwidth that travels over the carrier’s 3G/4G network. iGR focuses on this portion of the traffic because it is that cellular network load that is driving HSPA, HSPA+ and LTE migration, as well as the ongoing need to upgrade backhaul to Ethernet and fiber. (WiFi offload is, of course, a viable solution to mitigating the impact of stationary users on 3G/4G cell capacity, but other solutions may also work – distributed antenna systems and picocells are two examples.)

Forecasted Mobile Bandwidth Consumption

The following table shows iGR’s estimated mobile bandwidth consumption in 2011 as compared to our forecasted estimate for 2016. Over the forecast period, iGR expects the number of Medium and Heavy users to increase while the number of Light users decreases and the number of Extreme users grows minimally (as compared to 2011).

Table 1: U.S. Bandwidth Demand by User Category, 2011-2016 (MB/month)

Type of Connection 2011 2016 CAGR

Light 107.3 592.2 33.5%

Medium 233.9 2,107.8 46.2%

Heavy 864.7 4,149.3 30.5%

Extreme 4,454.2 8,006.7 10.8%

Total 5,660.0 14,856.0

Source: iGR, 2012


Figure 1 below illustrates the forecasted tenfold increase in bandwidth demand between 2011 and 2016. Note that this is for the entire U.S. market and represents megabytes (MBs) per month. It says nothing about the ability of the cellular data network to carry that traffic in the last mile over licensed spectrum. Most U.S. carriers are executing on LTE build out strategies and will likely have complete LTE networks (with some HSPA+ / CDMA fallback) in rural regions by 2016.

Figure 1: U.S. Mobile Bandwidth Demand (MB/month), 2011-2016

Source: iGR, 2012

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When Is Bandwidth Used?

Based on iGR’s primary research, there are three peak usage times: morning (commute); mid-day (lunch) and evening (commute) in an average individual’s cellular data use.

In the early morning, commuters might be on a train streaming Netflix or Pandora to their phones. Or, maybe they’re doing the same thing in a gym. During the midday lunch hours, users might be doing any number of Internet-related activities on either a smartphone or a tablet. In the evening hours, people might be commuting, exercising and/or socializing. Any and all of these activities might involve the use of cellular data.

iGR’s model further assumes that mobile bandwidth consumption during the day shifts over the forecast period (Figure 2). Note, too, that the overall amount of bandwidth consumed increases each year. The chart below shows the difference between 2011 hourly use and the forecasted use in 2016. In short, more usage begins to occur around the peak hours – not a great deal more, but some.

Figure 2: Change in Percent Usage per Hour, 2011 vs. 2016

Source: iGR, 2012

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Remember, as well, that bandwidth consumption per person per month is also increasing over the forecast period – from 466 MB/month/user in 2011 to an anticipated 2.4 GB/month/user in 2016. So, even in the off-peak hours more bandwidth is consumed. Again, note that this is the average amount of bandwidth per hour and is based on our per-month forecast.

Figure 3 illustrates the magnitude of the challenge facing mobile operators. The blue bars represent our estimate for the average demand per hour for cellular data (3G, 3.5G and 4G) per POP across the entire U.S. in 2011. The red bars represent iGR’s estimate for 2016.

Figure 3: Mobile Bandwidth Use by Time of Day (GB/hour/POP)

Source: iGR, 2012

The key question here is: Why is iGR assuming that peak usage will grow over the forecast period – in addition to the forecasted increase in bandwidth? The answer is threefold:

o Increasing adoption of devices over the forecast period. That is, more people are likely to use smartphones in 2016 (whether they want to or not) than currently.

o Increasing adoption of supplementary cellular data devices, such as tablets.

o Near complete LTE coverage by 2016.

More total devices on the cellular data network means more total mobile bandwidth consumed. More capable devices and faster networks mean that end users are more likely to use them.

2011 2016


iGR’s mobile bandwidth forecast also says nothing about where – geographically – the bandwidth will be consumed. Obviously, more total mobile bandwidth will be consumed in densely populated areas – urban and suburban areas – but rural areas may also experience an increase in mobile bandwidth usage since some carriers may use LTE as a broadband local loop replacement.

Within a given (sub)urban setting, the mobile bandwidth forecast does not address the macro cellular network “pain points” that emerge when people congregate in certain areas: a sports event, a concert, commuter trains, parks, coffee shops, theater and restaurant districts, universities, etc. When the population of a given square kilometer surges by several thousand – or tens of thousands – and most want voice/data/text access on their smartphone, then the cellular networks (since more than one operator would likely cover that area) need to accommodate that traffic surge.

Based on iGR’s research, most mobile operators design their networks so that they have, typically, about 15 percent additional capacity to handle peak hour traffic. Put another way, a given cellular network operates, on average, at about 85 percent of its total capacity. Because of this, the actual average sustainable level of data traffic at any point in time on a given cellular data network is closer to 6.5 percent.

When Bandwidth Demand Exceeds Average Network Capability

iGR also estimated when bandwidth demand might exceed the network’s average sustainable rate. Essentially, any positive numbers represent demand in excess of the average available capacity (6.5%).

At 8 AM, for example, iGR’s model shows that in 2011 demand exceeded average capacity by about 1.06 percent. By 2016, the forecast shows that demand at 8 AM will exceed average capacity by 1.46 percent. These peaks are those times when usage exceeds the average of what the network can handle at a given point in time. Such peaks equate to no service, poor service, or slow service. Consistent poor service could lead to higher customer churn.

In 2011, iGR’s model shows that a mobile operator experienced nearly 8 percent more traffic than its macro data network could handle. By 2016, iGR’s model indicates that the operator will have nearly 12 percent more traffic than its macro data network can handle. Note that this forecast assumes the deployment and increasing availability of LTE, and adoption by consumers, throughout the forecast period.

Despite LTE, then, iGR’s model shows that the macro network will be incapable of meeting (on average) peak hour cellular data demand. Put differently, simply deploying LTE to meet this excess bandwidth demand is insufficient – it alone is unlikely to solve the problem.


Where Pain Points Might Occur

Having shown where the pain points exist on a time of day basis, iGR also wanted to explore the other dimension of the problem – geography. To do that, iGR assumed a hypothetical City X of about 3 million inhabitants (Table 2, below). iGR also assumed a certain kilometers squared (KM2) for that city. iGR then calculated how much bandwidth might be consumed in a city that size – hence the GB per month per KM2 and the GB/day/KM2 estimates.

Put simply, this table shows the amount of mobile bandwidth demand per day per KM2 assuming the parameters of City X. For the purposes of this report, the city does not matter – it could be any city. This analysis is meant to show where the peaks / pain points are on a kilometer squared (KM2) basis.

Table 2: Mobile Data per Day (GB/day/KM2)

City X 2011 2012 2013 2014 2015 2016

Population, City X 3 3.06 3.12 3.18 3.25 3.31

KM

2 500 500 500 500 500 500

GB/month/KM

2 1,599.6 2,854.4 4,487.2 7,018.9 10,943.1 17,303.0

GB/day/ KM

2 57.13 101.94 160.26 250.68 390.82 617.97

Source: iGR, 2012

iGR then applied the GB/day/KM2 calculation to our model for how mobile bandwidth usage exceeds the average capacity of the macro network during the day. In other words, Figure 4 below shows how much demand in GB/hour/KM2 exists per person on a kilometer squared basis – above the average level that can be met by the macro network.

Put more simply, the following figure shows the demand that is in excess of what the carriers’ networks can deliver, on average, at any given time of day.


Figure 4: Bandwidth Demand on a KM2 Basis (GB/hour/KM2 over average bandwidth demand)

Source: iGR, 2012

Ultimately, the forecast shows that in 2011 in one KM2, a mobile operator would have to deliver 4.52 GB of traffic per day in excess of what their network could provide. By 2016, this figure increases to 67.3 GB/Day/KM2, an increase of over sixteen times.

On an average basis – and spread over an entire city network and over multiple operators serving a given city (since the model is based on KM2 not a single operator) the increasing “extra bandwidth” numbers might not seem that bad. However, there are several key points to remember.

o Mobile data usage (or voice or text) is never spread uniformly throughout time or space. iGR has shown the time aspect; the spatial spread will vary by city.

o Generally (and obviously) speaking, mobile usage is most likely to cluster around commute routes (car and train) and entertainment/social hubs.

o There are several ways to deal with “above average” bandwidth demand – get more spectrum; more channels in a given market; small cell approach (DAS, pico, metro); offload to WiFi (managed & owned by the operator); unmanaged / user-initiated WiFi “offload.”

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Factoring in Population Movement

Until now, iGR’s model assumed uniform usage across a metro area. Obviously, that is not the case. People commute; they go to lunch; they take business trips both within their state and across the country; they visit clients and prospects; they go to soccer games and school plays.

Consider a theoretical metro area (Figure 5). This is NASA photo of a major U.S. city. You can see roads (white) – the dark blue area is the “downtown” area. The green is suburbia.

Figure 5: Theoretical U.S. City

Source: NASA; iGR, 2012

The next graphic (Figure 6) shows this same metro area covered in cellular cells. This representation is basic and too uniform compared to a real city. It’s simply used to illustrate the overall concept. It also shows small orange circles that represent the smaller macro cells that are required to provide additional capacity and/or fill coverage gaps in the downtown area. In that downtown area, there is higher demand for service during the workday (more people in a smaller area) for a variety of reasons including: commute routes, daytime activities in the downtown area (lunch, meetings, intra-city travel, etc.).


The key point is that the population increases in downtown areas and decreases in the suburban areas. In the terms of iGR’s model, then, GB/hour/KM2 increases downtown and decreases where people live (during the day). In the evening, it shifts back.

With these types of variables factored in, the following graphic shows the (hypothetical) areas of very high data demand that are likely to occur in downtown/work areas during the day. These are represented by yellow dots.

The number, size, duration and intensity of these “bandwidth pain points” vary depending on population movement and will be different for every metro area. Chicago will look different from Houston, New York or San Francisco, Los Angeles and Seattle. Weather conditions, time of year and major events (conferences, sporting events, etc.) are also variables in the overall equation.

Figure 6: Theoretical Bandwidth Pain Points in City X

Source: iGR, 2012

With this groundwork in place, consider the following chart of bandwidth demand for City X – about 3 million people living in a metro area of 500 KM2. If iGR changes the “people movement” variable to assume that 50 percent of the population commutes to work and congregates in an area of, perhaps, 20 KM2 (to represent the core business district) then the mobile bandwidth demand in that 20 KM2 will increase by a factor of 12.5.


The reality is that people move around and they use voice/data while they do so. They are also using more smart devices that encourage more data-intensive applications – streaming, mapping, social networking, etc. And, during certain times of the day, people may move a little bit, but then turn on a device and watch Burn Notice on Netflix while eating lunch. If 10,000 people do that at the same time in a small geographic area, the macro cellular data network will probably have an issue.


Methodology

In part, the model presented in this report reflects iGR survey data conducted throughout 2011 and in early 2012. Typically, iGR surveys 1,000 U.S. consumers each month in order to generate timely insight on a variety of topics related to the wireless and mobile industry. The respondents are a representative, random sample of U.S. residents.

The model presented also reflects other proprietary research and knowledge iGR has acquired through its work with mobile operators, network equipment manufacturers and various other sources.

iGR End User Quantitative and Qualitative Studies

iGR regularly designs and implements primary research. iGR surveys a randomly selected, representative sample of U.S. end users using a census-based framework. iGR continually monitors our surveys for accuracy and updates them as new population information becomes available.

iGR routinely includes specialized questions at specific target markets to continually meet our clients’ needs and to provide current information and market trends.

When it comes to custom projects, examples of what iGR can provide are:

Domestic survey projects across most U.S. target demographics – with detail down to the ZIP code, if required.

International end user survey projects. To date, iGR have conducted primary research in China, Brazil, Egypt, Germany, the UK, Europe, India, Indonesia, Japan, Mexico and Canada.

Focus groups to assess end user reaction to current and future product.

iGR creates, codes, tests, deploys and fields surveys via a licensed, Web-based tool. iGR also maintains extremely close relationships with several major, high-quality panel companies.

As a result, once a client has approved the final survey iGR can field it within hours. Typically, our U.S. end user surveys complete in two days.

Upon survey completion, iGR can send raw data to the client immediately. The in-depth analysis takes longer, but every client will receive the raw data, cross-tabs and a thorough report summarizing and detailing the results and our findings.

Please contact Iain Gillott, president of iGR at (512) 263-5682 or by email at [email protected] for more details.

mailto:[email protected]


About iGR

iGR is a market strategy consultancy focused on the wireless and mobile communications industry. Founded by Iain Gillott, one of the wireless industry’s leading analysts, iGR researches and analyzes the impact new wireless and mobile technologies will have on the industry, on vendors’ competitive positioning, and on our clients’ strategic business plans.

Our clients typically include service providers, equipment vendors, mobile Internet software providers, wireless ASPs, mobile commerce vendors, and billing, provisioning, and back office solution providers. iGR offers a range of services to help companies improve their position in the marketplace, clearly define their future direction, and, ultimately, improve their bottom line.

Note that Iain Gillott currently serves as an independent director for Wmode, Inc.

A more complete profile of the company can be found at http://www.igr-inc.com/.

Disclaimer

The opinions expressed in this white paper are those of iGR and do not reflect the opinions of the companies or organizations referenced in this paper. All research was conducted exclusively and independently by iGR.

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