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

作     者：Hongyuan Gao Chenwan Li 

作者机构：School of Information and Communication Engineering Harbin Engineering University 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术(英文版))

年 卷 期：2015年第26卷第5期

页      面：897-907页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61102106) the China Postdoctoral Science Foundation(2013M530148) the Heilongjiang Postdoctoral Fund(LBH-Z13054) the Fundamental Research Funds for the Central Universities(HEUCF140809) 

主　　题：green cognitive radio parameter adjustment quantum computing bacterial foraging algorithm 

摘      要：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.