Distributed Cooperative Spectrum Sensing Based on Weighted Average Consensus

In this paper, we study the distributed spectrum sensing in cognitive radio networks. Using weighted average consensus algorithm, we develop a weighted soft measurement combining scheme without the centralized fusion center. After the measurement by the energy detector, each secondary user (SU) exchanges their own measurement statistics with its local neighbors, and chooses the information exchanging rate according to the estimated average signal-to-noise ratio (SNR). We prove the convergence of the consensus iteration, and each SU will hold the global decision statistics from the weighted soft measurement combining throughout the network. The proposed scheme is robust with respect to temporary communication link failures. Simulation results show our method has a better performance than the existing average consensus-based approach.