Optimization for cooperative spectrum sensing under Bayesian criteria

Spectrum sensing is one of the most challenging issues in cognitive radio systems. In this paper, optimization for cooperative spectrum sensing based on fusion of local hard decisions is studied. Given some prior knowledge of the targeted spectrum such as prior probability of occupancy by a primary user and the costs for missed detection and false alarm, a Bayesian detection problem is constructed to pursue the optimal fusion and local decision rules. A numerical iterative algorithm is proposed to approach the global optimum of the optimization problem.