Optimization of Cooperative Spectrum Sensing Using Genetic Algorithm (GA)

Abstract

Cognitive radio (CR) is a new paradigm wireless communication system which is use for efficient utilization
of radio frequency (RF) spectrum or RF channel for future wireless communication. Spectrum sensing is the key
component of cognitive radio technology. Cooperative spectrum sensing has proven its efficiency to detect spectrum
holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users The sensing
information from CR users combines at the Fusion center (common receiver) by soft combination or conventional hard
combination techniques. Soft combination has excellent performance but, it requires a lot of overhead for feedback
observation. In contrast, the conventional hard combination scheme requires only one bit of overhead, but it has worst
performance because of loss of information caused by local hard decisions. Weighting the coefficients vector is the
principal factor influencing the detection performance of the system in softened hard decision fusion based cooperative
spectrum sensing. In this paper, the use of Genetic algorithm (GA) based on the Neyman-Pearson criterion as a
significant method is proposed to optimize the weighting coefficients vector so that the probability of detection is
improved. The proposed technique investigates the best weighting coefficients vector. The performance of the GA-based
proposed method is examined and compared with soft combination technique as well as other conventional hard data
combination scheme through computer simulations. Simulation result shows that detection performance of proposed GA
based method is very close to equal gain combiner (EGC) which validate the strength of the proposed GA based method.