Efficient Spectrum Allocation in Cognitive Radio using Yellow Saddle Goatfish Algorithm

Abstract

Spectrum shortage and spectrum scarcity are the burning issues in wireless communication. To overcome these challenges, Cognitive Radio Networks (CRNs) can be deployed as an alternative technology to gainfully employ the underutilized radio spectrum of licensed users. In the current research work, multi-objective optimization based approaches are introduced for spectrum allocation in CRNs by minimizing interference, maximizing throughput, and improving network efficiency. Several constraints such as ambient noise, power transmission, and interference have been taken into consideration for equitable channel assignment to secondary users. Total network utilization and average capacity of secondary users are simultaneously optimized using multi-objective particle swarm optimization (MOPSO), multi-objective differential evolution (MODE), and non-sorting genetic algorithm (NSGA-II). The multiobjective optimization based spectrum allocation models have been simulated in a MATLAB environment and the obtained results are compared with a recently proposed yellow saddle goatfish algorithm (MOYSGA). The simulation results demonstrate that, total network capacity is inversely proportional to the average capacity of secondary users and further, the proposed approach provides fair channel allocation and optimal power to secondary users (SUs) under both the downlink and the uplink cases.