MOBILE DATA FORECASTING TOOLS AND METHODOLOGY TO IMPROVE ACCURACY AND OPTIMIZE PROFIT

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1 MOBILE DATA FORECASTING TOOLS AND METHODOLOGY TO IMPROVE ACCURACY AND OPTIMIZE PROFIT STRATEGIC WHITE PAPER As wireless network operators are faced with enormous CAPEX and OPEX decisions, effective and detailed forecasts are becoming increasingly strategic. Alcatel-Lucent Bell Labs has developed a tool that is being used to provide these detailed forecasts in some of today s networks. This paper illustrates some of the considerations that have led to the creation of this tool and outlines some of its capabilities for helping network operators forecast application-specific traffic at different locations.

2 TABLE OF CONTENTS Introduction / 1 Traditional Technology / 1 New Methodology / 3 Impact analysis of existing applications and devices / 4 Impact analysis of new devices and applications / 5 Recommended usage of the new methodology and the MDAM tool / 6 Input parameter settings / 6 Optimal MDAM parameter settings, sensitivity analyses and forecasting accuracy / 7 Conclusions / 7 References / 8

3 INTRODUCTION The rapid introduction of new mobile devices and data services presents many challenges to wireless service providers. Unlike voice services, whose traffic characteristics are well understood, new data services vary greatly in their behavior and demand on network resources, such as volume of data, signaling load and radio frequency (RF) airtime. Data volumes are exploding and impacting the ability of the network to support such traffic as well as potentially driving down gross margins significantly. The ability to accurately forecast data service traffic is critical to managing this data explosion. The issue is that not all devices are created equal, and the traffic profile for each network varies significantly based on the applications available in that network. A systematic bottom-up approach is required to analyze the impact of data services in terms of actual user behavior in order to create improved forecasts and greater profitability. This paper discusses a new methodology for mobile data forecasting developed by Bell Labs and the Mobile Data Analysis Model (MDAM) tool that can be used to create a forecast using the new methodology. An overview of the key features and settings of the MDAM tool are also included to help explain its capabilities. TRADITIONAL MOBILE DATA FORECASTING TECHNOLOGY Forecasting data traffic demands is a challenging task. Future loads are subject to many exogenous variables that can no longer be addressed by simply trending existing loads. Introducing a disruptive technology, such as new smartphones with multiple new applications, can result in unexpected consequences in a specific network. Wireless networks of several reporters experienced a significant increase in volume and signaling, which resulted in dropped calls, spotty service, delayed text and voice messages and slower download speeds. Many factors must be captured to accurately model mobile traffic, and some of these factors are not feasible to obtain. What is required is a systematic approach to forecasting that is not only rich enough to model individual application characteristics but also able to continually improve forecast accuracy by comparing with new measurements. Traditionally, wireless carriers have derived forecasts based on expected traffic demand per subscriber and the forecasted number of subscribers. These forecasts typically produce high-level information such as monthly data demand per subscriber. This type of information is appropriate at the marketing level because it relates to monthly data charges and subscription levels. However, this information is insufficient for network planning purposes because hourly load and other network details are not predicted. To create an effective forecast for network planning, a number of relevant factors should be taken into account. For example, subscribers use multiple applications from different device types, each with a unique set of traffic characteristics. Total monthly MB usage does not capture the fact that each such application has its own hourly traffic profile. The degree of coincidence or lack thereof determines the magnitude of the busy hour load, which is essential for engineering the network. 1

4 Geographic factors must also be considered. Base stations in residential and business locales have very different hourly traffic profiles because of their different subscriber base and the application mix. Therefore, a forecasting methodology must account for disparate application and subscriber behavior, and specifically it needs to account for how each device and the associated applications impact the three major resources in a wireless network: volume of data, signaling and RF airtime. Network capacity planning cannot be based on a single network-wide busy hour. The most costly part of a network is at its edges. In a mobile network, this is the Radio Access Network (RAN). Because base station traffic profiles vary greatly due to such factors as location (for example, residential, business, highway, and so on) and different application mixes, it is essential to size capacity for base stations and their associated RANs based on base station-specific loads. Figure 1 shows that daily peaks vary from one cell to another and that there are different patterns for the three major resources: traffic volume, signaling and RF airtime. The daily peak traffic varies from one cell to another, and this trend is more peaked than that of the core network peak. The traffic composition (for example, P2P, , web and so on) is not the same across cells. Also, there are different patterns for volume, signaling events and RF airtime. Figure 1. Total hourly traffic volume at cell sites (different colors correspond to different application types) Cell Site A Cell Site B 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 2

5 NEW MOBILE DATA FORECASTING METHODOLOGY Bell Labs methodology for mobile data forecasting takes into account the impact of subscriber traffic on each network element during different busy hours and considers the signaling and airtime in addition to the traffic volume that traditional methodologies use. This builds a bottom-up forecast that considers the usage of each device and application in the network and their impact on the network. The methodology uses actual data from the network to build the baseline and removes the guessing that exists in today s methodologies. Finally, the new model allows the marketing and planning teams to run many what-if scenarios for modeling the introduction of new devices, new applications and new data plans, to see the possible impact on the network. The MDAM tool can be summarized as follows: Data impact: The new methodology takes into account mobile data impact factors, including different busy hours and airtime vs. bearer path. Network feedback: Data is extracted on a per-user, per-device, per-application and per-cell basis and is used to continuously fine-tune the model. Bottom-up forecasting is based on usage by type of device and application and usage of airtime and bearers by cell site. Scenario analysis includes consideration of price plan vs. BH traffic and the interrelationship of new device introduction, changing take-rates, new application introduction and changing traffic per user (per app). The MDAM tool provides an interactive means of exploring various input parameters, such as shown in a highly simplified form in Figure 2, with modeled traffic as one of its outputs. The tool generates a future traffic model using forecasted subscriber quantities and take-rates coupled with application characterizations. There is a close coupling with the Alcatel-Lucent 9900 Wireless Network Guardian (WNG) to incorporate multi-dimensional characterization data for existing applications based on user-defined subscriber, application, device and network element groupings. An extensible application library enables flexible modeling with different app scenarios. The model output can be compared directly with network traffic data to provide additional feedback. The MDAM forecasting tool generates a future traffic model using forecasted subscriber data and associates the traffic rate with specific application types. The Alcatel-Lucent 9900 WNG provides multidimensional data characterizing subscriber, application, device and network element behavior. A library is developed, allowing extension to new application types. The forecasting tool provides convenient analysis, including hour-of-day visibility, day-of-week and holidays, and can further be compared with 9900 WNG data from live networks to assess model accuracy and evaluate sample data. The new Bell Labs mobile data forecasting methodology and the MDAM tool have a unique way of dealing with existing applications/devices in a network and future applications/devices not yet deployed. 3

6 Figure 2. Mobile Data Analysis Model overview Number of subscribers Site profile Network measurements Take-rate per app Interactive model Application characteristics Hourly traffic patterns Downstream traffic 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Impact analysis of existing applications and devices The methodology for modeling existing applications and devices uses actual traffic data from the specific network to build a baseline of the current traffic profile. The actual traffic data collected must be able to provide details on which applications each unique device (feature phone, smartphone, Apple iphone, Motorola Droid phone, Nokia Symbian Smartphone, USB dongle, PC card, m2m device, and so on) is running and the impact that each device has on network resources. In addition, the data collected shows how the traffic is distributed across each network element. Conventional traffic measurements of network elements do not provide information at the application/device level, but there are other collection and analysis tools that can provide this level of detail, such as the 9900 WNG, which is already deployed in many wireless networks. The 9900 WNG is a signaling probe-based system that collects more detailed data traffic information than what is available in typical traffic data collection systems. The 9900 WNG monitors the network at the packet level and creates mobile flow records that are rich in customer experience information. These flow records are processed in a variety 4

7 of ways, including performance monitoring, detection of anomalies and forecasting. In particular, the 9900 WNG provides hourly traffic data by application/device and subscriber. This information is aggregated in various ways to characterize application/ devices at the appropriate level for traffic forecasting purposes. The basic modeling principle in MDAM for existing applications and devices is to formulate a traffic model that permits basic trending as well as customization of applications or device loads based on a set of application- or device-specific parameters. These include application or device take-rate, device type distribution and call attempt load factor for each forecast period. Marketing people have the flexibility to modify the parameters based on the expected impact of carrier promotions or other factors that might influence subscriber behavior. For example, if a third-party application provider is launching a promotion campaign to stimulate usage of a specific application, the modeler would typically increase both the forecasted application or device take-rate, acknowledging that more subscribers will be using this application, as well as the call attempt load factor to account for greater frequency of use of the application. To provide a frame of reference for selecting parameter values, the 9900 WNG provides existing application take-rates by device as well as the device distribution planned; this is in addition to application or device hourly traffic loads. For each existing application and device pair, an hourly traffic load per subscriber is computed. This load can then be multiplied appropriately by the forecasted number of planned subscribers who have that device and use the application, to obtain hourly application or device traffic loads. Impact analysis of new devices and applications The absence of any traffic data for a new application that has not yet been deployed in the network requires a different approach. Each such application has an active and inactive time interval. The application transmits data only during the active interval. Given this information, the average data transmission rate during the intervals can be computed. Combined appropriately with the application attempt rate, traffic loads such as UL MB, DL MB and airtime can be calculated. MDAM contains a database of data applications, each containing default traffic characteristic values based on Wireless Forums, literature reviews and Alcatel-Lucent s experience with wireless carriers around the globe. MDAM also has the capability to model new applications and new devices and allow service providers to run theoretical what-if scenarios to determine the impact of a new device on a new application in the network even before the new application/device is actually deployed in the network. MDAM has several classifications for device groups, including feature phone, smartphone and mobile broadband (MBB)/PC-based. A new handset type, such as an iphone, may result in an increase in the percentage of subscribers in the smartphone classification and a corresponding reduction in the percentage of feature phones in the network. This scenario can be modeled by selecting appropriate values for MDAM s planned device distribution settings in each forecast period. 5

8 Applications running on these three categories of handsets can also have very different traffic characteristics. iphone users tend to be more traffic intensive than those with earlier smartphone devices. Therefore, if a substantial number of iphone subscribers are anticipated in a forecast period, the call attempt load factor for applications in the smartphone grouping should be increased. iphone introduction may also increase the number of smartphone subscribers using an existing application. This is easily reflected by increasing the application take-rates for smartphone devices. MDAM capabilities can include further granularity in the device distribution, permitting separate categories for individual phones such as RIM BlackBerry, iphone and Droid. MDAM also introduces forecasting of traffic for applications that have not yet been deployed in the network. A new applications database contains a variety of traffic profile information for data applications with built-in default values in this database for daily call attempts, durations and MB loads. These values are based on Alcatel-Lucent experience and external review of industry publications, and can be varied as necessary. By specifying the plan, application, device grouping and forecasted take-rates, and selecting an hourly call attempt count distribution that will prorate the daily call attempt count, the resulting new application traffic loads are computed and incorporated into the forecasts. Occasionally, a particular handset model design may result in excessive use of network resources, requiring the carrier to prohibit its use in the network. If, for example, this is a smartphone and users of the device are expected to just migrate to another sanctioned smartphone, then the device grouping distribution should not be changed. However, if subscriber defection is expected, then the device grouping distribution should be renormalized and the subscriber count forecast reduced. In either case, the traffic profile load for the device grouping containing the aberrant device should be reduced by applying a traffic call attempt load factor of less than one. Recommended usage of the new methodology and the MDAM tool A wireless carrier has multiple data plans, each supporting multiple applications and devices. Each plan requires separate application characterization. MDAM is designed for minimal user input and leverages 9900 WNG traffic data for existing applications as well as Bell Labs and Wireless Business Unit modeling expertise for new and to-be-deployed applications. Except for the forecasted number of planned subscribers and new application take-rates, all other required inputs have default values derived from either 9900 WNG data for existing applications or the MDAM new application database for new applications. Input parameter settings MDAM input parameters are set appropriately in accordance with an MDAM user s objectives. For example, a wireless plan may be promoting a particular application. This may stimulate traffic demand in several ways. The most obvious setting change is to increase the forecasted application take-rates for the promoted application. If the promotion is also expected to increase the overall number of planned subscribers, then forecasted subscriber counts should be increased based on marketing estimates of additional subscribers. Creating heightened awareness of a plan might also stimulate overall subscriber activity, in which case the call attempt load factors should be increased to values greater than one. Lastly, if the application is expected to produce migration of some plan subscribers from feature phones to smartphones, the forecasted device distribution parameter should be increased for smartphones and correspondingly reduced for feature phones. 6

9 Optimal MDAM parameter settings, sensitivity analyses and forecasting accuracy MDAM is designed to facilitate creating multiple reports, each based on different user-selected parameter settings. This what-if capability permits the user to assess the impact of various parameter settings on the forecasted traffic load. The best approach is to systematically vary parameters of interest to observe their impact on forecasted demands. A new traffic report can be created with each parameter change. The resulting set of created reports can then be reviewed to determine the sensitivity of parameters of interest on traffic load. An important model feature uses current data to improve forecasting accuracy: this requires comparing forecasted traffic statistics with actual statistics continuously. MDAM is particularly suited for supporting this comparison through its compare function. A user may select any existing MDAM project containing a forecast period that is now historic. Measured 9900 WNG traffic data corresponding to the forecast period and forecasted busy hour traffic loads at the base station or Radio Network Controller (RNC) level can be displayed in graphic and tabular formats. A 45-degree line is superimposed on a scatter plot of this data. The more data points that lie on or near the 45-degree line displayed in the plot, the more accurate the forecast. Points that deviate significantly from the 45-degree line should be analyzed subsequently for potential explanations of forecast inaccuracy, such as overly optimistic application take-rates or missing base station or RNC data. If explanations are identified, subsequent MDAM runs can be made with more accurate forecasts. Finally, with the MDAM analysis underway, a wireless operator can compute future traffic loads for use in subsequent capacity planning and capital budgeting exercises, which can improve the effectiveness (return on investment) of near-term equipment purchase. Longer-term forecasts can be used to improve capital budgeting, which is a longer-term exercise than capacity planning. The cost of equipment recommendations is computed to determine future CAPEX costs for budgetary purposes. CONCLUSIONS The new mobile data forecasting methodology and the MDAM tool can help service providers improve their forecasting accuracy and thereby improve their profitability. MDAM is data driven by the 9900 WNG, which provides detailed subscriber traffic profile information at the application and device level. It combines characterizations of existing and new applications with subscriber growth projections to create detailed traffic load forecasts. While positioned to facilitate traditional engineering functions such as capacity planning and capital budgeting, MDAM is also designed to support marketing studies and forecasting validation activities. 7

10 REFERENCES 1. Alcatel-Lucent, An Approach for Just-in-Time Radio Access Network Capacity Planning in CDMA Networks, internal report. 2. arstechnica.com, iphone overload: Dutch T-Mobile issues refund after 3G issues, June , 3. New York Times, Customers Angered as iphones Overload AT&T, September 2, Signals Research Group, The impact of smartphones on 3g network performance, May Signals%20Research%20Group.pdf Alcatel, Lucent, Alcatel-Lucent and the Alcatel-Lucent logo are trademarks of Alcatel-Lucent. All other trademarks are the property of their respective owners. The information presented is subject to change without notice. Alcatel-Lucent assumes no responsibility for inaccuracies contained herein. Copyright 2011 Alcatel-Lucent. All rights reserved. M (December)

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