基于萤火虫算法的多中继选择策略
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摘要
中继选择(RS)和功率控制是无线中继网络的2个重要组成部分.当中继节点以全功率协作和不协作时,中继选择等同于功率控制.因此,最佳信噪比(SNR)被描述称为0-1非线性整数规划问题(0-1 nonlinear programming integer problem,NLIP).文中提出了基于萤火虫算法(Glowworm Swarm Optimization,GSO)的多中继选择策略,仿真结果表明,基于GSO算法的多中继选择能够获得最佳信噪比值,且性能优于穷举搜索、单一RS方案及其他次优化方案.
Relay selection( RS) and power control are two essential parts in wireless relay network. RS is equal to power control in the condition that a relay cooperates with its full power or without any cooperation. Thus,the optimal signal-to-noise ratio( SNR) is interpreted as a 0-1 nonlinear programming integer problem( NLIP). A Glowworm Swarm Optimization( GSO) based multiple relay selection( MRS) scheme is proposed,which can fully obtain the optimal SNR value. Simulations results show that the proposed GSO-aided MRS scheme is superior to the conventional schemes,including the exhaustive search methods,single RS scheme and other suboptimal MRS schemes.
Relay selection( RS) and power control are two essential parts in wireless relay network. RS is equal to power control in the condition that a relay cooperates with its full power or without any cooperation. Thus,the optimal signal-to-noise ratio( SNR) is interpreted as a 0-1 nonlinear programming integer problem( NLIP). A Glowworm Swarm Optimization( GSO) based multiple relay selection( MRS) scheme is proposed,which can fully obtain the optimal SNR value. Simulations results show that the proposed GSO-aided MRS scheme is superior to the conventional schemes,including the exhaustive search methods,single RS scheme and other suboptimal MRS schemes.
引文
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[2]SENDONARIS A,ERKIP E,AAZHANG B.User cooperation diversity.Part I.System description[J].IEEE Transactions on Communications,2003,51(11):1927-1938.
[3]SENDONARIS A,ERKIP E,AAZHANG B.User cooperation diversity.Part II.Implementation aspects and performance analysis[J].IEEE Transactions on Communications,2003,51(11):1927-1948.
[4]LANEMAN J N,TSE D N C,WORNELL G W.Cooperative diversity in wireless networks:efficient protocols and outage behavior[J].IEEE Transactions on Information Theory,2004,50(12):3062-3080.
[5]HUNTER T E.Diversity through coded cooperation[J].IEEE Transactions on Wireless Communications,2006,5(2):283-289.
[6]BLETSAS A,KHISTI A,REED D P,et al.A simple cooperative diversity method based on network path selection[J].IEEE Journal on Selected Areas in Communications,2006,24(3):659-672.
[7]IBRAHIM A S,SADEK A K,SU W,et al.SPC12-5:relay selection in multi-node cooperative communications:when to cooperate and whom to cooperate with?[C]∥Global Telecommunica-tions Conference,GLOBECOM06.San Francisco:IEEE,2006:1-5.
[8]SOYSA M,SURAWEERA H A,TELLAMBURA C,et al.Partial and opportunistic relay selection with outdated channel estimates[J].IEEE Transactions on Communications,2012,60(3):840-850.
[9]JING Y,JAFARKHANI H.Single and multiple relay selection schemes and their achievable diversity orders[J].IEEE Transactions on Wireless Communications,2009,8(3):1414-1423.
[10]JING Y,JAFARKHANI H.Network beamforming using relays with perfect channel information[J].IEEE Transactions on Information Theory,2009,55(6):2499-2517.
[11]ATAPATTU S,JING Y,JIANG H,et al.Relay selection and performance analysis in multiple-user networks[J].IEEE Journal on Selected Areas in Communications,2013,31(8):1517-1529.
[12]DU M,LEI X,WU Z.A simplified glowworm swarm optimization algorithm[C]∥2014 IEEE Congress on Evolutionary Computation(CEC).Beijing:IEEE,2014:2861
[13]KUO R J,YANG C Y.Simulation optimization using particle swarm optimization algorithm with application to assembly line design[J].Applied Soft Computing,2011,11(1):605-613.
[14]LIANG Y,LEUNG K S.Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization[J].Applied Soft Computing,2011,11(2):2017-2034.
[15]LEI X,HUANG X,ZHANG A.Improved artificial bee colony algorithm and its application in data clustering[C]∥Bio-Inspired Computing:2010 IEEE Fifth International Conference on Theories and Applications(BIC-TA).Shanghai:IEEE,2010:514-521.
[16]KRISHNANAND K N,GHOSE D.Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications[J].Multiagent and Grid Systems,2006,2(3):209-222.
[17]KRISHNANAND K N,GHOSE D.Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations[J].Robotics and Autonomous Systems,2008,56(7):549-569.
[18]KRISHNANAND K N,GHOSE D.Glowworm swarm optimisation:a new method for optimising multi-modal functions[J].International Journal of Computational Intelligence Studies,2009,1(1):93-119.
[19]KRISHNANAND K N,GHOSE D.Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions[J].Swarm Intelligence,2009,3(2):87-124.
[20]HANSEN P.Methods of nonlinear 0-1 programming[J].Annals of Discrete Mathematics,1979,5(8):53.
[21]KRISHNANAND K N,GHOSE D.Detection of multiple source locations using a glowworm metaphor with applications to collective robotics[C]∥Proceedings of the 2005IEEE Swarm Intelligence Symposium.Piscatawag:IEEE,2005:86.
[22]ALJARAH I,LUDWIG S A.A new clustering approach based on glowworm swarm optimization[C]∥2013 IEEE Congress on Evolutionary Computation(CEC).Cancun:IEEE,2013:2642-2649.
[23]HE D X,LIU G Q,ZHU H Z.Glowworm swarm optimization algorithm for solving multi-objective optimization problem[C]∥Proceedings of the 9th International Conference on Compu-tational Intelligence and Security.Leshan:IEEE,2013:11-15.
[24]唐思腾,刘紫燕,帅暘.基于萤火虫算法的多中继功率分配[J].电讯技术,2014,54(10):1407-1412.TANG S T,LIU Z Y,SHUAI Y.Power allocation for multirelay cooperative systems based on glowworm swarm optimization algorithm[J].Telecommunication Engineering,2014,54(10):1409.