An improved weighted cooperative spectrum sensing in cognitive radio networks

Spectrum sensing is the key technology in a cognitive radio network to coordinate the secondary users. The optimal linear soft combination framework of cooperative spectrum sensing includes the local spectrum sensing and optimal fusion center. This paper studies the optimal weighted cooperative spectrum sensing. We apply a new LINWPSO algorithm for optimal weight setting to improve the sensing performance. Simulation results and analysis demonstrated that the LINWPSO method has better CROC performance than the MDC and SNR based methods, but this algorithm needs more computation complexity. If the number of particles for particle swarm optimization algorithm is large enough, the performance of LINWPSO method outperforms PSO method. In addition, performance analysis on different signal to noise ratio (SNR) environment is discussed.