Efficient UMTS Location Update and Management Based on Wavelet Neural Networks

Abstract

Mobility management in wireless cellular networks is gaining more attention because of the increase in usage of mobile devices. As the number of mobile users increases rapidly, there is a need for efficient location management. Location management (LM) tracks mobile devices and locate them prior to establishing incoming calls. As the cell size becomes smaller the signalling cost increased in both location update and paging. In order to deal with the signalling cost issues, an efficient user activity based location management technique (UALM) is introduced in this paper. UALM is a profile based LM scheme, where the network takes the users past movement pattern and makes decisions on the future update and paging. This paper presents a novel intelligent Wavelet Neural Network (WNN) based UALM learning strategy to solve the LM problem in UMTS networks. A systematic comparative analysis is made with the existing location management schemes. The results show that the proposed WNN based UALM learning decreases the signalling cost. The proposed technique has the potential to reduce the network signalling cost and total LM cost that must be made to locate the users. © Springer International Publishing Switzerland 2014.