Location management in cellular networks using soft computing algorithms

Abstract

The enormous increase in mobile subscribers in recent years has resulted in exploitation of wireless network resources, in particular, the bandwidth available. For the efficient use of the limited available bandwidth and to increase the capacity of the network, frequency re-use concept is adopted in cellular networks which led to increased number of cells in the network. This led to difficulty in finding the location of a mobile user in the network and increase in the signalling cost. Location management deals with keeping track of an active mobile terminal in a specific area while minimizing the cost incurred in finding the mobile terminal. The existing location management is done by grouping the cells based on subscriber density. Location management strategies are based on user mobility and incoming call arrival rate to a mobile terminal, which implies that the location management cost comprises of location update cost and paging cost. Reporting cell planning is an efficient location management scheme wherein few cells in the network as assigned as reporting cells, which take the responsibility of managing the location update and paging procedures in the network. Therefore, the need of the hour is to determine an optimal reporting cell configuration where the location management cost is reduced and thereby maintaining a trade-off between location update and paging cost. The reporting cell discrete optimization problem is solved using genetic algorithm, swarm intelligence technique and differential evolution. A comparative study of these techniques with the algorithms implemented by other researchers is done. It is observed that binary differential evolution outperforms other optimization techniques used for cost optimization. The current work can be extended to dynamic location management to assign and manage reporting cells in real-time implementable fashion.