الفهرس 
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Abstract
With the fast development of mobile communication technology, it is becoming important to improve the quality of service (QOS) of Personal Communication Service (PCS) networks. To accomplish such aim, it is essential to give more attention to Mobility and handoff management. Handoff is one of the problems, which affects the quality of the call delivery during users’ mobility from cell to another. The Proposed Mixed Movement Predictor (MMP), which uses to reduce the impact of the handoff problem through a perfect prediction of the future movements for network Mobile Terminals (MTs). MMP gives adaptable use of the limited resources of PCS networks. It combines two unique predictors, which are; (i) Ant Based Prediction (ABP), and (ii) Fuzzy Based Predictor (FBP). When the considered MT has no or insufficient pre-registered trajectories (history) pass through the cell (cc) where the call has been initialize, ABP uses Ant Colony Optimization (ACO) based on the history of the other MTs inside the current Registration Area (RA). ABP consists of two Parts, namely; (i) the Ant Prediction Engine (APE), which relies on the movement history of the other MTs to predict the future movement of the considered MT, and (ii) the Sectored Diurnal Mobility Model (SDMM) design, which predicts the exact future sector and cell of the considered MT. FBP is employed if the considered MT has sufficient history at cc. FBP employs a fuzzy inference system, which combines evidence from regional, topological, profitable, and date/time factors. MMP has been compared against recent mobility prediction techniques for PCS networks. Experimental results have shown that MMP outperforms its competitors as it introduces the maximum prediction accuracy as well as the minimum prediction time, handover latency and resource reservation.