Study On Intelligent Location Management Strategies In Wireless Mobile Networks

In wireless mobile networks, which support personal and terminal mobility, users can use any mobile terminals (MTs) with the users' personal telecommunication numbers to communicate with anyone anytime anywhere. Generally, when a call to a mobile user arrives, the network needs to exactly know the user's location to establish a communication route between the caller and the mobile user so that the call is transferred to the mobile user in a short period of time. With the tendency of mobile users' explosive growth, bandwidth limitation and signaling traffic overload on the network have become a serious problem. Therefore, how to effectively and efficiently track mobile users turns into one of the research frontiers over the world.Location management, which is used in mobile communication networks to track mobile terminals, plays an important role in resource management of wireless mobile networks. It has two basic operations: location update and paging. Based on reinforcement learning, a multi-stage and adaptive intelligent paging scheme embedded a reinforcement learning mechanism, referred to as an adaptive intelligent paging (ALP) below, is proposed in this paper, which learns MTs' location information obtained in previous paging processes and dynamically adjusts the cells included in each paging stage so that paging cost is reduced. In addition, simulation and performance analysis are made to compare the AIP with the basic paging scheme, used in the existing mobile communication networks such as GSM (Global Systems for Mobile Communication), and the paging scheme without learning mechanism. Simulation results illustrate that the AIP is the best among the three schemes, and that under the AIP the paging cost can be reduced considerably and the delay can be controlled within a reasonable range.