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低复杂度的MIMO系统粒子滤波检测

郑贱平 白宝明 王新梅

郑贱平, 白宝明, 王新梅. 低复杂度的MIMO系统粒子滤波检测[J]. 电子与信息学报, 2009, 31(1): 87-90. doi: 10.3724/SP.J.1146.2007.01070
引用本文: 郑贱平, 白宝明, 王新梅. 低复杂度的MIMO系统粒子滤波检测[J]. 电子与信息学报, 2009, 31(1): 87-90. doi: 10.3724/SP.J.1146.2007.01070
Zheng Jian-ping, Bai Bao-ming, Wang Xin-mei. Low-Complexity Particle Filtering Detection for MIMO Systems[J]. Journal of Electronics & Information Technology, 2009, 31(1): 87-90. doi: 10.3724/SP.J.1146.2007.01070
Citation: Zheng Jian-ping, Bai Bao-ming, Wang Xin-mei. Low-Complexity Particle Filtering Detection for MIMO Systems[J]. Journal of Electronics & Information Technology, 2009, 31(1): 87-90. doi: 10.3724/SP.J.1146.2007.01070

低复杂度的MIMO系统粒子滤波检测

doi: 10.3724/SP.J.1146.2007.01070
基金项目: 

国家自然科学基金(60472098,60502046)资助课题

Low-Complexity Particle Filtering Detection for MIMO Systems

  • 摘要: 该文通过降低采样大小和信号检测搜索空间给出了两种低复杂度的多输入多输出(MIMO)系统粒子滤波(PF)检测方法:球形约束PF和多层映射PF。在球形约束PF中,首先基于迫零原则求得所需的球形约束,然后利用该球形约束减少粒子滤波过程中每一级重要性采样生成的粒子数。多层映射PF则采用多层映射将大小为4L的正交幅度调制(QAM)星座划分为L个4-QAM星座的级联以降低信号检测的搜索范围。计算机仿真结果表明,第1种方法能够在大发送天线数的情况下保持系统性能且有效地降低粒子滤波的计算复杂度;而第2种方法能够以较低的错误性能损失为代价获得计算复杂度的极大降低。
  • Doucet A, de Freitas J F G, and Gordon N. Sequential MonteCarlo Methods in Practice. New York, Springer-Verlag, 2001.[2]Doucet A, Godsill S, and Andrieu C. On sequential MonteCarlo sampling methods for Bayesian filtering[J].Statist.Comput.2000, 10(3):197-208[3]Djuric P M, Kotecha J H, and Zhang J, et al.. Particlefiltering. IEEE Signal Processing Magazine, 2003, 20(5): 19-38.[4]Doucet A and Wang X. Monte Carlo methods for signalprocessing. IEEE Signal Processing Magazine, 2005, 22(6):152-170.[5]Huang Y, Zhang J, and Djuric P M. Bayesian detection forBLAST[J].IEEE Trans. on Signal Processing.2005, 53(3):1086-1096[6]Dong B, Wang X, and Doucet A. A new class of soft MIMOdemodulation algorithms[J].IEEE Trans. on Signal Processing.2003, 51(11):2752-2763[7]Golub G H and Van L C F. Matrix Computations (3rdedition). Baltimore, MD, USA, Johns Hopkins UniversityPress, 1996, Chapters 2 and 5.[8]Liu J and Chen R. Sequential Monte Carlo methods for dynamicsystems[J].J. Amer. Statist. Assoc.1998, 93(5):1032-1044[9]Chen R and Liu J. Mixture Kalman filter. J. Amer. Statist.Assoc. (B), 2000, 62(3): 493-509.[10]Kitagawa G. Monte Carlo filter and non-Gaussian nonlinearstate space models[J].J. Comput. Graph. Statist.1996, 5(1):1-25[11]Liu J S. Monte Carlo Strategies in Scientific Computing. NewYork, Springer-Verlag, 2001, Chapter 5.[12]Hochwald B M and ten Brink S. Achieving near-capacity on amultiple-antenna channel[J].IEEE Trans. on Commun.2003,51(3):389-399[13]De Jong Y vo L C and Willink T J. Iterative tree searchdetection for MIMO wireless systems[J].IEEE Trans. onCommun.2005, 53(6):930-935
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出版历程
  • 收稿日期:  2007-06-29
  • 修回日期:  2007-10-29
  • 刊出日期:  2009-01-19

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