A new strategy for mobility prediction in the PCS network

Abstract

Mobility prediction is one of the main challenges that faced Personal Communication Service (PCS) network. It is probable for many users to move among cells (coverage areas) during their calls. Therefore, the network needs to predict their next location in order to reserve another resource in that next cell to keep their calls going on. In this paper, a new strategy is proposed for mobility prediction named Mixed Mobility Prediction (MMP). It is composed of two predictors. The first one is named Association Rules Predictor (ARP), and the second one is called Weighted Ant Colony Predictor (WACP). In ARP the prediction is based on Association rules in data mining and detecting the time of calls. In WACP the prediction is based on Ant Colony (AC) in swarm intelligence. In addition to that, roads lead to their predicted next locations, and priority of famous places found in those locations. Finally, MMP merges the decisions of both predictors to get the final accurate decision in the absence of sufficient history for a MT. The proposed approach outperformed the compared the state-of-arts methods in terms of; Prediction Accuracy (PA), and Quality of Measure (QM).