A Kalman-filter based location management strategy for cellular networks

In this paper, we propose a new predictive location management strategy that reduces the update cost while restricting the paging load optimized for mobiles roaming with traceable patterns. Enhanced with directional predictive capabilities offered by Kalman filtering, new update boundaries are assigned to better reflect the movement patterns of individual mobiles upon location registration. Thus, while complying with the required delay constraints, QoS measures such as throughput will not need to be sacrificed as a result of increasing update threshold. The contribution of this paper is two-fold: (1) to propose a distribution model that is capable of describing a wide range of movement patterns with varying correlation between traveling directions and (2) to show the capabilities (in terms of reliable performances) of the Kalman filter in predicting future movement patterns. Simulation results have successfully demonstrated the ability of the Kalman filter in assigning update boundaries capable of reflecting a mobile's roaming characteristics. The performance gains achieved mainly through a significant reduction in the number of updates indicate its potential for promoting better bandwidth conservation.