Markovian Modeling of Wireless Trace Data

Abstract

With the increased availability of wireless networks, performance evaluation of these networks has become more important in recent years. Adequate models of wireless networks have to account for the user mobility as the movements of the users can have a large influence on the network performance. In many cases data recorded from real networks serves as basis to model the user mobility. Therefore, appropriate distributions have to be found to model characteristics like dwelltimes from the real world data and different general distributions like Lognormal, Weibull or Pareto have been used in the past. In this paper we present an extensive comparison for the fitting quality of those general distributions with Phase-type distributions (PHDs). Our results suggest that in most cases even small PHDs with four or five states yield a better approximation of the real data than the general distributions.