An adaptive wireless paging scheme using Bayesian network location prediction model

Various paging strategies have been proposed to improve the efficiency of paging management. However, most of the schemes ignore how to obtain location predictions or are too complex for real systems. In this paper, we propose a new adaptive paging scheme designed according to the mobility pattern and location probabilities. The Bayesian Network is used as the location prediction model which describes a broad class of mobility patterns. The probability distribution of a Mobile Terminal's location is derived on the condition that the incoming calls form a Poisson process and the cell holding time has an exponential probability distribution. The paging strategy is implemented using a heuristic algorithm which can adaptively change according to the given mobility pattern and traffic parameters. The running time of the strategy is only Θ(N) where N describes the number of cells in a Tracking Area. Experimental results show that the adaptive paging scheme also achieves a low paging cost compared to other schemes proposed in the literature.