Minimizing the Detection Error in Cooperative Spectrum Sensing Using PSO

Abstract

Cognitive radio (CR) is a new paradigm in wireless communication system which is use for efficient
utilization of radio frequency (RF) spectrum or RF channel for future wireless communication. Cooperative spectrum
sensing is a key technology in cognitive radio networks (CRNs) to detect spectrum holes by combining sensing result of
multiple cognitive radio users. This sensing information from CR users combines at the Fusion center (common receiver)
by soft combination or conventional hard combination techniques. Sensing error minimization is an important aspect of
cooperative spectrum sensing that needs attention. In this paper, the use of particle swarm optimization (PSO) under
MINI-MAX criterion is proposed to optimize the weighting coefficients vector of energy level of spectrum sensing
information so that the total probability of error is minimized. The particle swarm optimization (PSO) algorithm
investigates the best weighting coefficient vector which minimizes total probability of error. The performance of the PSO
based method is analysed and compared with conventional soft decision fusion schemes like EGC as well as hard
decision fusion method like AND,OR, Majority etc. Simulation results show that the proposed scheme minimizes the
detection error compared to conventional soft decision fusion schemes.