Cooperative Spectrum Sensing Using Genetic Algorithm for Optimal User Detection in Cognitive Radio Networks Cooperative Spectrum Sensing Using Genetic Algorithm for Optimal User Detection in Cognitive Radio Networks

Cognitive Radio is an modern technology that is used to get the picture of spectrum and cope themselves to operate the secondary users who are unlicensed to share the spectrum with licensed primary users. Cooperative spectrum sensing allows unlicensed access whenever possible to the unexploited portions of the licensed spectrum by exploiting the spatial diversity among multiple secondary users. Due to cooperation overhead and correlated shadowing the performance of cooperative sensing gets derivate. If we select suitable members of secondary users who exhibit a small correlation with each other, we can establish balance between cooperation overhead and the performance for the sake of cooperation. Based on the false-alarm and missed-detection probabilities, the paper proposes the performance of the cooperative spectrum sensing detection under the correlated log-normal shadowing scenario. Adaptive genetic algorithm is used for the optimization of the number of secondary users required. The proposed method will find the optimum number of secondary users participating in cooperation. Finally, we presented the simulation results to prove the proposed scheme is effective.