LOCATION PREDICTION IN CELLULAR NETWORK USING NEURAL NETWORK

The mobility management is an important issue in the cellular network, where it is deal managing of the limited frequency BW, and managing the roaming of mobile station (MS). It consists of two parts, the first called hand-off, which deals with the frequency channel allocation and conserve the call during move between two adjacent cells. The second part called location management (LM), which is deal with how to track an active MS within the cellular network. LM will burden the network with many messages of paging and location update to make the network know the location of MS at any time. Many researchers attempt to improve the LM by using neural networks to perform location prediction. In this paper, we will use back propagation multilayer neural network to learn the subscriber movement, and then using this trained network to predict the new location of the subscriber. The main aim of this paper is to reduce the total cost of LM by using the prediction of subscriber location instead of using the traditional LM schemes. We get a more than 69% correct prediction for the random walk mobility pattern as will see in the results.

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