Robust Spectrum Sensing Employing PSO

Abstract

In cognitive radio network (CRN) cognitive radio users (CRUs) try to utilize the radio spectrum of the licensed primary uses (PUs) without creating disturbances. To do that efficient spectrum sensing is one of the key jobs at the SUs part. As the individual user sensing performance is not considered authentic and reliable in the multiple channel effects of fading, shadowing, and receiver uncertainties, therefore, cooperative spectrum sensing (CSS) provides an optimal solution to be deployed in these environments. One major problem for CSS is to deal with abnormal sensing reports of the reporting users. A malicious user (MU) reports false sensing data to the fusion center (FC) so that to create confusion about the PU's existence. In this paper particle swarm optimization (PSO) algorithm is tested to reduce the impact of MUs in the FC decision. The cooperative users report their channel findings to the FC, where PSO tries to find the existence of any abnormality in the sensing data. The results are confirmed through extensive simulation at different combination of MUs that shows the proposed scheme effectiveness.