A user pattern learning strategy for managing users' mobility in UMTS networks

Third-generation mobile systems provide access to a wide range of services and enable mobile users to communicate regardless of their geographical location and their roaming characteristics. Due to the growing number of mobile users, global connectivity, and the small size of cells, one of the most critical issues regarding these networks is location management. In recent years, several strategies have been proposed to improve the performance of the location management procedure in 3G mobile networks. In this paper, we present a user pattern learning strategy (UPL) using neural networks to reduce the location update signaling cost by increasing the intelligence of the location procedure in UMTS. This strategy associates to each user a list of cells where she is likely to be with a given probability in each time interval. The implementation of this strategy has been subject to extensive tests. The results obtained confirm the efficiency of UPL in significantly reducing the costs of both location updates and call delivery procedures when compared to the UMTS standard and with other strategies well-known in the literature.