CASE STUDY Intelligent Small Cell Trial Intel® Architecture Rethinking the Small Cell Business Model Wireless data traffic continues to application services on the base grow at an unprecedented rate. station. Trial results show that Cisco reported that in 2011 mobile deploying intelligence at the access data traffic experienced a 2.3 point can radically change the traffic fold increase, reaching over 597 profile, making the cost of deploying petabytes per month.1 To meet this small cells increasingly viable. demand, the wireless industry has recognized a need to add small cells Test results showed that deploying to the deployment options. A small applications that improve the cell can be defined as an access point compression and transcoding of video that provides a higher capacity over a stream, coupled with predictive and proactive data analytics substantially In 2011 mobile data traffic small coverage area. reduced peak traffic loads. Reducing experienced a 2.3 fold The business economics for peak traffic loads has two profound increase, reaching over 597 deploying small cells differs from that effects. First, it significantly improved petabytes per month.1 of traditional micro and macro cells. the subscriber’s experience as By definition, many more small cells loading web pages and downloading are required to provide the same videos is notably faster. Second, coverage as one macro base station. it reduced the required data rate The site acquisition, installation and of the backhaul connections. For backhaul facility costs associated service providers that lease their with deploying lots of small cells are backhaul connections, the savings are considerably higher than the cost of considerable. deploying a macro cell. For small cell deployments to be financial viable, a This paper discusses several new economic cost model is needed. applications and techniques that reduced the peak traffic on a base Over the past year, Intel and its station by over 45 percent. It then partners have been conducting small uses an industry recognized business cell trials that incorporate intelligence model to quantify one of the key at the base station. This intelligence benefits, backhaul operational costs. can be used to run network and Caching content at non-peak times when the for reducing backhaul traffic and the backhaul links are not being fully associated operational costs. In the next five years, Mobile utilized. Predictive cache reduces 1 data is expected to grow 19 fold. the amount of backhaul traffic Encoding and decoding of real time Analysis of this data is revealing when multiple users request the traffic streams is computationally trends in usage patterns, purchasing same content. It also evens out the intensive. It requires an intelligent patterns, and demographics. Early traffic profile during the day, which platform at the access point and trends suggest that there are many reduces the load at peak times. a server at the other end of the situations where multiple users Proactive caching directly reduces backhaul link to do the encoding access the same content. Examples the backhaul traffic at all times of the and decoding. include a release of a new version of day, including peak times. Backhaul Newer transrating techniques can the Apple* iOS, popular TV shows, connections typically guarantee vary the reduced bit rate based on viral videos, and geographically a maximum data rate for a fixed the availability of network bandwidth. relevant content such as maps and price. These reduction in the peak In other words, transrating can restaurant guides. load, directly reduce the backhaul further reduce the bit rate when the Analyzing subscriber traffic and operating costs. network becomes congested at peak caching content locally at the times. As mentioned above, reducing Transrating Video Content access point has the potential to the peak load has the largest impact significantly reduce backhaul traffic. Typically, the backhaul network has on reducing the backhaul costs. This is particularly notable on days a fixed data rate. The challenge Users are uploading and downloading when there are popular events such for mobile service providers is to video content in a range of different as a sports game or at locations find an effective way to transmit formats. This is particularly where people congregate and watch multiple variable bit rate video noticeable when the same bit-stream the same video content on their streams over a constant bit rate is distributed to devices that support individual devices. In these situations channel. Transrating takes any video different decoders. In addition to the mobile network will be inundated format and reduces it to a lower bit transrating the video traffic over the with requests for the same content. rate encoded traffic stream while backhaul link, a service provider can There are two primary forms of minimizing the impact on video leverage the local storage capabilities storing local content; predictive quality. Transrating can adapt to of the intelligent access point and caching and proactive caching. changing bandwidth conditions. store video content in several popular Predictive caching is when a network As demand for bandwidth grows, video formats. This enables service provider predicts the type of data transrating can reduce the video bit providers to deliver the same content that the user will want and stores stream to a minimum to allow more to multiple users in the format it locally. Proactive caching is when video streams to be processes. best suited to their device. It also users request web pages that are Over 50 percent of traffic over enables the operator to deliver a subsequently stored locally. mobile networks is video, and this higher quality video experience to It is important to understand that number is expected to grow to over premium high paying users and a predictive and proactive caching 66 percent in the next five years.1 lower bandwidth video experience to impact backhaul traffic in different This growth in video traffic makes non-premium users. ways. Predictive caching downloads transrating increasingly important 2 Trials realize the benefits of 17.6% intelligent small cells In 2011, to test the concept of ProactiveProactive an intelligent small cell in a live environment, Intel worked with Edge PredictivePredictive 54.4% Datacomm* to deploy small cells on 16.0% Image a train in the town of Kenilworth, CompressionImage Compression England. Intelligent small cells were deployed on a four carriage train, OtherOther and two 3G links from different service providers were aggregated 12.0% together to provide the backhaul 2 link. The intelligent small cell used Figure 1. Percentage of transmitted data that is cached plus image compression proactive and predictive caching techniques to predict the data that should be stored locally on the train. an average of 200 users accessed Facebook was the most accessed Predictive data included the BBC* the network in any one day. Each site, followed by the BBC and Google. news, train time tables, film reviews, of these users transmitted and Many web pages, such as Facebook, and maps. The intelligent small cells received on average 22 megabytes have repetitive elements that can be also identified and compressed of data per day. Figure 1 shows stored in cache. Caching this content JPEG videos. that on a consistent basis, over 17 means that these pages will load percent of the transmitted data was very quickly on user devices. This is Data was collected over a 25 day proactively cached and 16 percent an important benefit to the user, as period. The trial results showed that was predictively cached. they will have the perception that the network is very responsive to their requests. Data to and from Train In this trial, the total backhaul traffic Bits per second was reduced by over 45 percent, and 1280.0 K the operator reported a 22 percent saving in operating expenditure 960.0 K (OPEX). Figure 2 shows a sample 640.0 K of user data transmissions taken over a 24 hour period. The blue 320.0 K line shows data transmitted on the downlink from the network to the 0.0 K user. The green line shows data 22 20 18 16 14 12 10 8 6 4 transmitted on the uplink from the user to the network. The peak data Blue is receive Time of Day 24 hour clock Green is transmit (data from right to left) rate was approximately 1.6 Mb/s on the downlink. Figure 2. Data transmitted over a 32 hour period 3 Integration of Wi-Fi in small Another important trend is the Network OPEX savings in urban cells growth in Wi-Fi hotspots deployment London exceed 22 percent by cellular service providers in the The mobile industry has been To understand the impact of past year. Service providers like integrating Wi-Fi and cellular intelligent small cells in a large China Mobile* and China Telecom* networks for over ten years. cellular network deployment, Intel that have both announced their Significant effort has been placed worked with the independent intention to deploy one million into defining standards that enable market research firm Wireless hotspots. A large driver behind these seamless coexistence and handover 20/20.4 Wireless 20/20 have an announcements is the desire by between these technologies. In the industry recognized business case mobile service providers to offload past year new trends have emerged analysis tool called WiROI*. The WiROI subscriber data from the cellular which suggest that Wi-Fi should be tool provides in-depth analysis of networks and on to the lower cost integrated in small 3G and 4G cells. the capital and operational expenses Wi-Fi networks. Most people are not surprised that for the deployment of broadband over 92 percent of smartphones The integration of Wi-Fi into a small wireless networks.