Optimization of Congnitive Radio Networks Using Computational Intelligence

Abstract

With the advent of high data rate applications and services, the spectrum demand has increased enormously. Moreover, most spectrum bands are underutilized and need be efficiently utilized for new wireless applications. Cognitive communication is promising technology to deal with this spectrum shortage problem. Cognitive radio is an intelligent device capable to utilized licensed bands for unlicensed users incurring the minimum interference to licensed users. The efficient utilization of spectrum resources which includes maximizing throughput and minimizing interference is a key research challenge in cognitive radio networks. This thesis presents efficient spectrum assignment algorithms in order to maximizes throughput, minimize the interference incurred to licensed users and interference among secondary users. Moreover, accumulative cost to buy licensed networks is reduced. The resource allocation is a multi-objective optimization problem.A fuzzy logic based ant colony algorithm is proposedin order to achieve above objectives in cognitive radio heterogeneous network (CRHN). This study presents a fuzzy logic based ant colony algorithm (FLACSA) for interference minimization towards Primary Users (PUs) and overall cost reduction ofsecondary users (SUs) to buy spectrum. Performance of FLACSA and ant colony system algorithm (ACSA) is evaluated againstparticle swarm optimization (PSO) and genetic algorithm (GA) approaches in literature and proposed scheme achieved very attractive results. A repair process based channel assignment algorithm is proposed and spectrum utility is optimized using differential evolutional based particle swarm algorithm and modified genetic algorithm in CRN. The performance of proposed algorithms is investigated against various parameters of network and evaluated against the other studied algorithms in literature. The results of proposed algorithms outclass all other algorithms studied in literature.