Cooperative Spectrum Sensing Optimization Using Meta-heuristic Algorithms

Spectrum sensing helps to sense the unutilized spectrum in an opportunistic manner for cognitive radios. The various cognitive radios work in a cooperative manner to improve the efficiency of sensing by making use of the heterogeneity of multiusers. Meta-heuristic methods are being widely used for optimization problems in different domains. The selection of the best meta-heuristic algorithm results in high performance. These algorithms can also be used for optimizing the spectrum sensing in cognitive radio network. In this paper, two meta-heuristic algorithms namely grey wolf optimization (GWO) and dragonfly algorithm (DA) are used for cooperative spectrum sensing in cognitive radio network. These algorithms evaluate the optimal weighting vectors used in the data fusion center. This is further used for allocation of spectrum to the secondary users. The proposed methods are compared with genetic algorithm and particle swarm optimization based cooperative spectrum sensing optimization. The results show that both the proposed methods for cooperative spectrum sensing optimization based on DA and GWO have better convergence rate. Also, the maximum probability of detection is achieved with DA and GWO. Further it is observed that GWO performs even better than DA.