OPTIMIZATION OF CHANNEL SELECTION IN COGNITIVE HETEROGENEOUS WIRELESS NETWORKS

Abstract

Cognitive radio technology has been developed to improve radio resource utilization of the wireless network environment. Recently, new wireless services have been spread widely, and the mobile traffic is rapidly increasing. The idea of cognitive radio technology is to utilize radio resources by using dynamic spectrum access. We propose an autonomous and decentralized optimization method for cognitive radio networks, in which users autonomously select the most appropriate wireless network, by sensing available networks, selecting the best one, and reconfiguring themselves to the selected networks. We use the Hopfield Neural Networks which is autonomous and decentralized optimization technique. The idea of this method is based on the decreasing property of energy function of the Hopfield Network. We run the proposed algorithm on mobile terminal to select the wireless network that maximizes the throughput and minimizes the power consumption of the device. Numerical simulations show the validity of the proposed algorithm