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
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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)