Secondary User Selection Scheme Using Adaptive Genetic Algorithms for Cooperative Spectrum Sensing Under Correlated Shadowing

Cooperative spectrum sensing has been shown to be an effective approach to improve the detection performance by exploiting the spatial diversity among multiple secondary users (or unlicensed users). However, due to correlated shadowing and cooperation overhead in practical cognitive radio networks, it is desired to select an appropriate set of secondary users which have little correlation with each other to participate in cooperation so as to achieve the effective tradeoff between detection performance and cooperation overhead. In this paper, we first study the hypothesis testing model and detection performance of cooperative spectrum sensing under the correlated log-normal shadowing scenario. Afterwards, based on whether the false-alarm and missed-detection probabilities are constrained, three optimization problems are formulated to find the optimal set of secondary users participating in cooperation, which take into account the tradeoff between detection performance and cooperation overhead. Then the solutions using adaptive genetic algorithms are presented for the optimization problems. Finally, simulation experiments demonstrate that our proposed schemes are very effective.