Predictive Distance-Based Mobility Management for PCS Networks

This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. Instead of the constant velocity fluid-flow or the random-walk mobility model, a more realistic Gauss-Markov model is introduced, where a mobile’s velocity is correlated in time to a various degree. Based on the Gauss-Markov model, a mobile’s future location is predicted by the network based on the information gathered from the mobile’s last report of location and velocity. When a call is made, the network pages the destination mobile at and around the predicted location of the mobile and in the order of descending probability until the mobile is found. A mobile shares the same prediction information with the network and reports its new location whenever it reaches some threshold distance away from the predicted location. We describe an analytical framework to evaluate the cost of mobility management for the proposed predictive distance-based scheme. We then compare this cost against that of the regular, non-predictive distance-based scheme, which is obtained through simulations. Performance advantage of the proposed scheme is demonstrated under various mobility and call patterns, update cost, page cost, and frequencies of mobile location inspections.