Recent Efficient Iterative Algorithms on Cognitive Radio Cooperative Spectrum Sensing to Improve Reliability and Performance

Abstract

In cognitive radio (CR), cooperative spectrum sensing (CSS) has been extensively explored to be accounted for in a spectrum scanning method that permits secondary users (SUs) or cognitive radio users to utilize discovered spectrum holes caused by the absence of primary users (PUs). This paper focuses on optimality of analytical study on the common soft decision fusion (SDF) CSS based on different iterative algorithms which confirm low total probability of error and high probability of detection in detail. In fact, all steps of genetic algorithm (GA), particle swarm optimization (PSO), and imperialistic competitive algorithm (ICA) will be well mentioned in detail and investigated on cognitive radio cooperative spectrum sensing (CRCSS) method. Then, the performance of CRCSS employing GA-, PSO-, and ICA-based scheme is analysed in MATLAB simulation to show superiority of these schemes over other conventional schemes in terms of detection and error performance with very less complexity. In addition, the ICA-based scheme also reveals noticeable convergence and time running performance in comparison to other techniques.