Cooperative Spectrum Sensing Using Bat Algorithm in Cognitive Radio

Cognitive radio offers a promising solution to the problem of spectrum scarcity caused by heavy use of the radio frequency spectrum. The efficiency of cognitive radio largely depends on the correct implementation of spectrum sensing. In this paper, the detection probability, which is the criterion for accurately detecting the presence of spectrum holes and primary users (PUs), is optimized using the bat algorithm (BA). A cooperative spectrum detection scheme based on soft decision fusion is used, where appropriate weights are assigned to each secondary user. This scheme determines the optimal weight vector coefficients using the bat algorithm. The generated optimally weighted fusion scheme is compared with conventional fusion schemes and particle swarm optimization (PSO). It has been observed that BA provides a better detection probability.