Spectrum Sensing in Cognitive Radio Networks using Particle Swarm Optimization Algorithm

Abstract

Cognitive radio (CR) has emerged as a consistent solution to the issue of spectrum scarcity. On CR networks, the spectrum sensing mainly deals with the reliable detection of primary user signals. The cooperative spectrum sensing utilizes spatial diversity between CR networks for enhancing the performance of sensing accuracy. Usually, a weight value is given to each and every CR in cooperative spectrum sensing network and then the global decision threshold values are formulated as an inhibited multi-objective optimization issues. In the present work, a Particle Swarm Optimization (PSO) algorithm is utilized to solve the optimization concerns in the multi-objective systems. In experimental phase, the proposed optimization algorithm is related to three existing optimization algorithms such as, Cat Swarm Optimization (CSO), Bacterial Foraging Optimization (BFO), and Genetic Algorithm (GA) in light of throughput, probability of detection and energy consumption. In addition, different tests are performed in assessing the stability of the simulation outcome offered by the PSO algorithm.