accurate mobility modeling and location prediction based on pattern analysis of handover series in mobile networks
The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. The optimal placement/deployment of e.g. UMTS, IEEE 802.16 WiMAX base stations or IEEE 802.11 WLAN access points is based on user distribution and traffic characteristics in the service area. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose a novel Markov chain based model capable of utilizing user's movement history thus providing more accurate results than other models in the literature. The new model is applicable in real-life scenarios, because it relies on information effectively available in cellular networks (e.g. handover history). The complexity of the proposed model is analyzed, and the accuracy is justified by means of simulation.
mobility modeling prediction handover