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基于离散粒子群算法的CDMA多用户检测方法 被引量:11

Discrete particle swarm optimization algorithm for multi-user detection in CDMA communication system
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摘要 研究了离散粒子群算法,并将其应用于CDMA多用户检测问题,提出一种基于离散粒子群优化算法的CDMA多用户检测的方法。该方法应用一种新的选择和分区搜索的策略,改进搜索的质量和效率。分析以及实验仿真表明该方法具有计算复杂度低且可以得到较好误码率性能的特点,为寻求新的多用户检测方法提供了思路。 Research it on discrete space, an discrete particle swarm optimization algorithm for the multiuser detection(MUD) problem in code division multiple access(CDMA) communication system was described. This approach using a new select method and search space partition strategy to improve the search quality and efficiency, analyses and simulation results show our approach has low computational complexity, and the BER property of the algorithm is better than the conventional detector, to find a new method to solve the problem of MUD in CDMA.
出处 《通信学报》 EI CSCD 北大核心 2005年第7期109-113,122,共6页 Journal on Communications
基金 安徽省高等学校青年教师科研资助计划项目(2005jq1032zd) 安徽省自然科学基金资助项目(03042208)
关键词 码分多址 多用户检测 粒子群优化算法 误码率 code division multiple access multi-user detection particle swarm optimization algorithm (PSO) bit error rate
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参考文献11

  • 1LUPAS R. Near-far resistance of multi-user detectors in asynchronous channels[J]. IEEE Trans on Commun, 1990, 38(4):496-508.
  • 2VERDU S. Optimum multi-user asymptotic efficiency[J]. IEEE Trans on Commun, 1986,34(9):890-897.
  • 3KENNEDY J, EBERHART R. Particle swarm optimization[A]. Proc IEEE Iht Conf on Neural Networks[C]. Perth, 1995. 1942-1948.
  • 4EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[A]. Proc 6th Int Symposium on Micro Machine and Human Science[C]. Nagoya, 1995.39-43.
  • 5SHI Y H, EBERHART R. Parameter selection in particle swarm optimization[A]. Proc of the 7th Annual Conf on Evolutionary Programming[C]. Washington D C, 1998. 591-600.
  • 6CLERC M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization[A]. Proc of the Congress of Evolutionary Computation[C]. Washington D C, 1999.1951-1957.
  • 7LOVBJERG M, RASMUSSEN T K, KRINK T. Hybrid particle swarm optimizer with breeding and subpopulations[A]. Proc of the 3rd Genetic and Evolutionary Computation Conf[C]. 2001.
  • 8KENNEDY J, EBERHARTR. A discrete binary version of the particle swarm algorithm[A]. Proc IEEE Iht Conf on Systems, Man, and Cybernetics[C]. Orlando, 1997. 4104-4108.
  • 9VERDU S. Computational complexity of optimum multi-user detection[J]. Algorithmic, 1989,4(3):303-312.
  • 10倪梁方,郑宝玉.自适应RBF神经网络在CDMA移动通信上行链路功率控制中的应用研究[J].通信学报,2003,24(12):42-51. 被引量:14

二级参考文献35

  • 1维特比 A J.CDMA扩频通信原理[M].北京:人民邮电出版社,1997..
  • 2[31]Eberhart R, Hu Xiaohui. Human tremor analysis using particle swarm optimization[A]. Proc of the Congress on Evolutionary Computation[C].Washington,1999.1927-1930.
  • 3[32]Yoshida H, Kawata K, Fukuyama Y, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment[J]. Trans of the Institute of Electrical Engineers ofJapan,1999,119-B(12):1462-1469.
  • 4[33]Eberhart R, Shi Yuhui. Tracking and optimizing dynamic systems with particle swarms[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Hawaii,2001.94-100.
  • 5[34]Prigogine I. Order through Fluctuation: Self-organization and Social System[M]. London: Addison-Wesley,1976.
  • 6[1]Kennedy J, Eberhart R. Particle swarm optimization[A]. Proc IEEE Int Conf on Neural Networks[C].Perth,1995.1942-1948.
  • 7[2]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[A]. Proc 6th Int Symposium on Micro Machine and Human Science[C].Nagoya,1995.39-43.
  • 8[3]Millonas M M. Swarms Phase Transition and Collective Intelligence[M]. MA: Addison Wesley, 1994.
  • 9[4]Wilson E O. Sociobiology: The New Synthesis[M]. MA: Belknap Press,1975.
  • 10[5]Shi Yuhui, Eberhart R. A modified particle swarm optimizer[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Anchorage,1998.69-73.

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