Mobile data mining for radio resource management in wireless mobile networks

We have introduced in [15] a predictive call admission control and resource reservation (PCACRR) technique for wireless mobile networks. In this technique, the behavior of the user is recorded over a period of time and analyzed to generate the mobility models (profiles) of the users. The generated mobility models are used to predict the future movement of the users in order to perform the call admission and the resource reservation. A local mobility model can be generated for the individual user and a global mobility model can be generated for a group of users. We used the combination between the local and the global mobility; models to compare the performance of this technique with other techniques. In this paper, we focus on studying the performance of PCAC-RR technique comparing three types of mobility models: 1) the local mobility model only, 2) the global mobility model only, and 3) the combination between local and global mobility models. Also, in this paper we explain the simulation environment in details and how to study the performance of the presented technique using each of these mobility models. Simulation results show that using the hierarchical structure of local and global mobility, models improves the performance significantly.