Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Improved Genetic Algorithm for Channel Allocation with Channel Borrowing in Mobile Computing
IEEE Transactions on Mobile Computing
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
Optimum testing of multiple hypotheses in quantum detection theory
IEEE Transactions on Information Theory
Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments
Wireless Personal Communications: An International Journal
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Review of Robust Cooperative Spectrum Sensing Techniques for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
Cyclostationarity-Based Decision Reporting Scheme for Cooperative Spectrum Sensing
Wireless Personal Communications: An International Journal
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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.