On the accuracy of mobility modelling in wireless networks

Abstract

-In a wireless mobile network, two major problems arise: the poor performance of the wireless layer and the effect of user mobility. The problems related to limited available radio bandwidth and radio channel errors seem to be solved by CDMA technique applied in 3G mobile systems. The bandwidth that is provided by CDMA air interface is enough for mobile multimedia applications. However such applications are sensitive to the degradation of QoS parameters. Graceful degradation could happen when too many mobiles arrive to the same radio cell. To avoid such situations the Call Admission Control (CAC) has to limit the number of newly accepted connections. Terminal mobility causes problems in call admission control because the number of active mobile terminals in a cell is a random variable. In this paper we introduce a new method to predict the number of terminals in each cell. Based on this information a more effective CAC algorithm can be applied in order to ensure user's satisfaction.

A Simulation Based Mobility Models Comparative Study

“…Random Waypoint Mobility Model [1,2], Random Walk Mobility Model [3], Normal Walk Mobility Model [4,5], Smooth Mobility Model [6,7], are the random models. And the terrain aware models are the Markovian Movement Model [8,9], Normal Markovian Mobility Model and the Mask Based [10,11,12] Mobility Model. Based on these simulations an evaluation of each model is done by quantifying its characteristics and evaluating them in metrics.…”

The SONG (SOlutions for Next Generation Mobile Systems) Project

“…what parameters should be measured in a real network, in order to determine the model's parameters) and accuracy of these models are not examined. We have developed a new Markov mobility model especially appropriate for accurate prediction of the future number of mobile users in an urban environment [4]. The other advantage of our model is its simplicity, making it useful for numerical analysis.…”

“…We are trying to describe analytically the relationship between the optimal prediction distance and the user's velocity [4].…”