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Optimization of Cooperative Spectrum Sensing in Ad-Hoc Cognitive Radio Networks

Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.