An Improved Optimal Linear Weighted Cooperative Spectrum Sensing Algorithm for Cognitive Radio Sensor Networks

In order to improve the sensing accuracy of the Cognitive Radio Sensor Networks and reduce the interference to the primary user, this paper proposes an improved optimal linear weighted cooperative spectrum sensing scheme on the assumption that the report channel is not ideal. Through mathematical modeling, the spectrum sensing problem is ultimately converted into a constrained nonconvex optimization problem, and the chaotic harmony search (CHS) algorithm is to be used to find the optimal weighting vector value. The simulation results show that the proposed linear cooperative spectrum detection scheme based on the CHS algorithm has better performance than HS, SFLA, EGC, MRC, and MDC algorithm. In addition, the influence of local noise power, report channel noise power, and report channel gain on the performance of the algorithm is analyzed by simulation. The results show that local noise power has greater impact on the sensing performance.