Trajectory estimation based on mobile network operator data for cellular network simulations

EURASIP Journal on Wireless Communications and Networking, Oct 2016

In this paper, we present a framework for estimating trajectories of cellular networks users based on mobile network operator data. We use handover and location area update events of both speech and packet data users captured in the core network of the Austrian MNO A1 to estimate the subscribers’ mobility behavior. By utilizing publicly available data, i.e., environmental information, road infrastructure data, transmitter power ranges and antenna characteristics, our approach allows the estimation of subscriber trajectories for both urban and semi-rural environments with a good accordance to the actual trajectories. Additionally, we present a method to estimate a particular subscriber’s movement velocity, on the basis of mentioned data. Furthermore, we propose a methodology to estimate when a particular user started or ended a speech or packet data session during his journey, based on mobility-related network events. With this, our framework enables the creation of reproducible mobility situations for cellular network simulations at system level.

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Trajectory estimation based on mobile network operator data for cellular network simulations

Ostermayer et al. EURASIP Journal on Wireless Communications and Networking Trajectory estimation based on mobile network operator data for cellular network simulations Gerald Ostermayer 0 Christoph Kieslich 0 Manuel Lindorfer 0 0 University of Applied Sciences Upper Austria , Softwarepark 11, 4232 Hagenberg , Austria In this paper, we present a framework for estimating trajectories of cellular networks users based on mobile network operator data. We use handover and location area update events of both speech and packet data users captured in the core network of the Austrian MNO A1 to estimate the subscribers' mobility behavior. By utilizing publicly available data, i.e., environmental information, road infrastructure data, transmitter power ranges and antenna characteristics, our approach allows the estimation of subscriber trajectories for both urban and semi-rural environments with a good accordance to the actual trajectories. Additionally, we present a method to estimate a particular subscriber's movement velocity, on the basis of mentioned data. Furthermore, we propose a methodology to estimate when a particular user started or ended a speech or packet data session during his journey, based on mobility-related network events. With this, our framework enables the creation of reproducible mobility situations for cellular network simulations at system level. Mobility; Trajectory estimation; Modeling; Simulation; Cellular networks 1 Introduction During the standardization and development process of cellular systems, it is necessary to evaluate the performance of new features that are to be tested. Since it is not feasible to implement an entire test system for every planned feature in the early development stages, simulations are the only method to get performance figures that help to assess the value of the new features. Some features and algorithms strongly depend on the mobility of the subscribers (e.g., power control, handover, scheduling); therefore, dynamic system level simulations are necessary to incorporate the mobility of the subscribers. This applies not only for new systems; also already deployed systems are continuously improved over their lifetimes. And again, performing simulations is the proper method to rate features and algorithms under evaluation. In this case, the simulations should be based on the real network, i.e., the real cell deployment in the real environment (comprising the real street network) with the actual offered traffic. Additionally, the subscriber mobility behavior should be incorporated as realistic as possible. In that case, it is very beneficial to use the mobile network operator’s (MNO) information related to the subscribers’ mobility, i.e., anonymous handover (HO) and location area updates (LAU). In our work, we use HO and LAU events of speech (GSM) and packet data (GPRS, UMTS, LTE) users captured in the core network of the Austrian MNO A1. The location accuracy of these events is limited to the coverage area of the concerned cells. Additionally, we use freely available data sets about the environment, the base station (BS) configuration, antenna characteristics, transmitter power ranges, etc. The mentioned data sets provide us with a sequence of sample points of the subscriber’s trajectory where each sample point has a location inaccuracy based on the cell coverage area. In this work, we estimate trajectories and the velocity of subscribers based on the available data sets and compare these trajectories and velocities with those the subscribers moved along in reality. Additionally, we use specific events captured in the core network to determine when a certain © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. speech or packet data session started or ended. This information provides a time and spatial frame for modeling network traffic, whereas the actual traffic model to be applied can be selected on demand at a later point in time. This generic solution allows the creation of reproducible mobility situations for real network simulations, whereby the traffic model becomes exchangeable. Figure 1 outlines the basic concept of our system: a trajectory is generated based on a given set of LAU and HO events. These events are recorded asynchronously, i.e., each event is logged on occurrence. A HO event occurs whenever the mobile terminal changes its serving cell whereas a LAU event is issued whenever a mobile terminal changes its location area. With that, we have information about the trajectory as sampling points (timelocation tuples) with a certain accuracy in space domain and exact in ti (...truncated)


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Gerald Ostermayer, Christoph Kieslich, Manuel Lindorfer. Trajectory estimation based on mobile network operator data for cellular network simulations, EURASIP Journal on Wireless Communications and Networking, 2016, pp. 242, Volume 2016, Issue 1, DOI: 10.1186/s13638-016-0718-x