Novel intelligent soft computing techniques for location prediction in mobility management

Voice and data services are provided by cellular networks to the users with mobility. To bring services to the mobile users, the cellular network is capable of tracking the locations of the users, and allowing user movement during the conversations. With the growing number of mobile users, global connectivity, and the small size of cells, location management is one of the most critical issues regarding these networks. The objective of a dynamic location management system is to maintain a low paging cost in the communication system. As the data associated with the communication system is a purely non-stationary, therefore soft-computing approach this time playing major roles for achieving the real objective of the location management system. In this research article, an attempt has been made to apply PPN (Polynomial Perceptron Network) for development of an intelligent location management system. In this work a comparison of several locations management techniques are performed under identical terminal mobility model and network characteristics. From the simulation result it is seen that MLP (Multilayer Perceptron) has better performance than PPN network, however PPN network takes very less CPU time comparing with MLP system.