Cooperative spectrum sensing for cognitive radios based on a PA-GABC algorithm

Spectrum sensing plays a critical role in cognitive radio networks. A good sensing scheme can reduce the false alarm probability and increase the detection probability, and thus improves utilization. In this paper, in order to avoid the defect that cooperative spectrum sensing based on Particle Swarm Optimization (PSO) algorithm is easy to trap into a local optimization. We propose a population adaptive Gbest-guided artificial bee colony (PA-GABC) algorithm and it is applied to cooperative spectrum sensing for cognitive radios field. Simulations are performed to compare the performance of the PA-GABC algorithm and traditional artificial bee colony (ABC) algorithm. The performance of the proposed cooperative spectrum sensing method based on the PA-GABC and that of the cooperative spectrum sensing method using particle swarm optimization (PSO) algorithm are also compared. Results show that the proposed PA-GABC-based cooperative spectrum sensing method is efficient and stable, and it gets higher detection probability than the ABC-based method and the PSO-based method under the same probability of false alarm.