Sensor and channel selection for cooperative sensing in multichannel cognitive radio systems

This paper investigates the issue of how to balance the tradeoff between sensing performance and sensing costs/rewards for cooperative sensing in a multichannel cognitive radio system. Two cases of practical interest are studied. In the first case, the number of available sensors is assumed to be sufficient for detecting available frequency channels. For this case, we study the problem of selecting appropriate sensors to minimize the cost of detecting all the available channels subject to sensing performance constraints. The problem can be solved by using a branch-and-bound algorithm. In the second case, the number of available sensors is assumed to be insufficient for detecting all the available channels. For this case, we study the problem of selecting appropriate channels to maximize the sensing rewards subject to sensing performance constraints. Since the computational complexity of solving this problem optimally is fairly high, we propose a greedy algorithm as a low complexity solution to the problem. We further validate the effectiveness of the proposed algorithm via Monte-Carlo simulations.