Modified Dynamic Network Selection Algorithm for Heterogeneous Overlaid Networks

To meet wide expectation and growing demand of mobile users, operators have started to deploy different overlapping radio access network technologies. In heterogonous overlaid networks, network selection decision is delegated to mobile users to avoid processing and signaling overhead. In user centric network selection, users select the network selfishly to maximize their utility and cause undesirable network states. Selecting optimal network to meet user experience with enhancement in network performance under dynamically varying network environment is a real challenge. The existing online dynamic network selection algorithm (DNSA) selects network only by considering change in user demand type to meet user experience. Network assisted selection is another existing algorithm which selects network based on dynamic change in network state and improves network performance. In this paper modified dynamic network selection algorithm (m-DNSA) is proposed which learns network state using Boltzmann-Gibb’s reinforcement algorithm, dynamic change in user demand type using Markov Decision Process and selects optimal network. The m-DNSA also considers cost of handover between different networks for selecting network. Hence, it jointly improves performance of network and user experience. The proposed algorithm is investigated in UMTS-WiMAX-WLAN overlaid networks with different type of services and various economical categories of users. Simulation results show that the proposed m-DNSA improves QoS of the users and also increases throughputs of individual network compared with existing DNSA.