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Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

Journal of Systems Engineering and Electronics 2015年第5期26卷 897-907页

作者： Hongyuan Gao Chenwan Li School of Information and Communication Engineering Harbin Engineering University

Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this *** superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.

关键词： green cognitive radio parameter adjustment quantum computing bacterial foraging algorithm

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