Optimal Linear Soft Fusion Schemes for Cooperative Sensing in Cognitive Radio Networks

In this paper, three optimal linear soft fusion schemes for cooperative sensing are proposed on the basis of corresponding optimality criteria, namely Neyman-Pearson (N-P), deflection coefficient maximization (DCM), and linear quadratic optimization (LQO). Multiple cooperative secondary users (SU) in the cognitive radio (CR) network simply serve as relays to provide space diversity for the fusion center (FC) to obtain the global test statistic. After the ideal optimal fusion weights are acquired, iterative weight setting strategies are utilized to implement them in practice. Analysis and simulation results illustrate that the proposed N-P and DCM schemes yield significant improvements on the sensing performance and the iterative weight setting algorithm can effectively approach the ideal performance of these two schemes. As for the LQO scheme which operates on the received signal covariance matrices merely, it is capable of providing satisfactory performance with sufficient cooperative SUs.