Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (23): 151-153.DOI: 10.3778/j.issn.1002-8331.2010.23.043

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Blind identification of time-varying channel in chaotic communication

FAN Yong-quan1,2,ZHANG Jia-shu2   

  1. 1.School of Mathematics and Computer Engineering,Xihua University,Chengdu 610039,China
    2.Key Lab of Signal and Information Processing,Southwest Jiaotong University,Chengdu 610031,China
  • Received:2009-01-19 Revised:2009-04-24 Online:2010-08-11 Published:2010-08-11
  • Contact: FAN Yong-quan

混沌通信中时变信道的盲辨识

范永全1,2,张家树2   

  1. 1.西华大学 数学与计算机学院,成都 610039
    2.西南交通大学 信号与信息处理重点实验室,成都 610031
  • 通讯作者: 范永全

Abstract: With the boundedness of chaotic signal the Set-Membership Generalized Least Mean Square(SM-GLMS) algorithm has been successfully used to blind channel equalization in chaotic communications.In this paper,the SM-GLMS is further applied to time-varying channel identification and its convergence property is also justified,finally a novel GLMS algorithm with Variable Forgetting Factor(VFF-GLMS) is proposed.Simulation results show that the performance of VFF-GLMS is comparable with that of SM-GLMS.In comparison with conventional GLMS algorithm both of them provide significant improvement in terms of steady state performance and tracking ability to the variation of channel parameters.

Key words: blind identification, set-membership, Least Mean Square(LMS), chaos, time-varying channel

摘要: 利用混沌信号的有界性,集员广义LMS(SM-GLMS)算法成功用于混沌通信中的盲信道辨识。进一步将其应用于时变信道的盲辨识,分析了该算法的收敛性能,并提出了一种新的基于时变衰减因子的GLMS(VFF-GLMS)算法。结果表明VFF-GLMS和SM-GLMS算法性能相当,并且比常规的GLMS算法具有更好的稳态性能和时变参数跟踪能力。

关键词: 盲辨识, 集员, 最小均方(LMS), 混沌, 时变信道

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