Improved soft fusion-based cooperative spectrum sensing using particle swarm optimization

In soft-decision fusion- (SDF-) based cooperative spectrum sensing, weighting the coefficients vector is the main factor affecting the detection performance of cognitive radio networks. In this paper, the use of particle swarm optimization (PSO) algorithm as a prominent technique is proposed to optimize the weighting coefficients vector. The proposed PSO-based scheme opts for the best weighting coefficients vector, leading to improved detection performance of the system. The performance of the proposed method is analyzed and compared with genetic algorithm- (GA-) based technique as well as other conventional SDF schemes through computer simulations. Simulation results validate the robustness of the proposed method over all other SDF techniques.