Predictive distance-based mobility management for multidimensional PCS networks

This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. In this scheme, a mobile's future location is predicted by the network, based on the information gathered from the mobile's recent report of location and velocity. When a call is made, the network pages the destination mobile around the predicted location. A mobile makes the same location prediction as the network does; it inspects its own location periodically and reports the new location when the distance between the predicted and the actual locations exceeds a threshold. To more realistically represent the various degrees of velocity correlation in time, a Gauss-Markov mobility model is used. For practical systems where the mobility pattern varies over time, we propose a dynamic Gauss-Markov parameter estimator that provides the mobility parameters to the prediction algorithm. Based on the Gauss-Markov model, we describe an analytical framework to evaluate the cost of mobility management for the proposed scheme. We also present an approximation method that reduces the computational complexity of the cost evaluation for multidimensional systems. We then compare the cost of predictive mobility management against that of the regular, nonpredictive distance-based scheme, for both the case with ideal Gauss-Markov mobility pattern and the case with time-varying mobility pattern.