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基于子空間分解的快速盲均衡解調(diào)算法

羅來源,  肖先賜

羅來源, 肖先賜. 基于子空間分解的快速盲均衡解調(diào)算法[J]. 電子與信息學報, 2003, 25(6): 838-843.
引用本文: 羅來源, 肖先賜. 基于子空間分解的快速盲均衡解調(diào)算法[J]. 電子與信息學報, 2003, 25(6): 838-843.
Luo Laiyuan, Xiao Xianci. The Blind Equalization Based on subspace method with fast convergence[J]. Journal of Electronics & Information Technology, 2003, 25(6): 838-843.
Citation: Luo Laiyuan, Xiao Xianci. The Blind Equalization Based on subspace method with fast convergence[J]. Journal of Electronics & Information Technology, 2003, 25(6): 838-843.

基于子空間分解的快速盲均衡解調(diào)算法

The Blind Equalization Based on subspace method with fast convergence

  • 摘要: 基于高階統(tǒng)計的盲均衡算法需要大量的觀測數(shù)據(jù)，當觀測數(shù)據(jù)有限或信道變化較快時，由于模型失配將使均衡性能嚴重下降，因此短數(shù)據(jù)、快收斂是近年來盲均衡技術(shù)研究的主要方向之一。本文討論了基于子空間分解的信道識別和盲均衡技術(shù)，利用卡爾曼濾波方程，給出了一種快速收斂的盲均衡解調(diào)算法，算法只需要較少的數(shù)據(jù)樣本，仿真結(jié)果表明該方法是有效的。
    Abstract: A large of number sample data is needed by the blind channel equalization based on HOS (High Order Statistics). The performance of equalization was found to be poor in fast change environment or with short data samples. The blind equalization using second order statistics has drawn considerable attention recently. The performance with fast convergence makes it useful in the mobile communications or short-wave communications. The algorithm for subspace method based on the blind channel identification/equalization using Kalman filter equation is presented, which needs a few data. Simulation results show that the algorithm is effective.
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  • Y. Sato, A method of self-recovering equalization for multilevel amplitude modulation, IEEE Trans. on Comm., 1975, COM-23(6), 679-682.[2]A. Bcnvniste, M. Goursat, Blind equalizers, IEEE Trans. on Comm., 1984, COM-32(8), 871-883.[3]D. Hatzinakos, Blind equalization using decision feedback prediction and tricepstrum principle, Signal Processing, 1994, 36(3), 261-276.[4]S. Haykin, Adaptive Filter Theory, New Jersey, Prentice-Hall, Third Edition, 1991, Ch.18.[5]L. Tong, G. Xu, T. Kailath, A new approach to blind identification and equalization of multipathchannel, Proc. of the 25th Asilomar Conference on Signals, Systems, and Computer, Pacific Grove, CA., Nov. 1991, 340-349.[6]H. Liu, G. Xu, A determinstic approach to blind symbol estimation, IEEE Signal Processing Letters, 1994, 1(12), 205-207.[7]E. Moulines.[J].P. Duhamel, J. Cardoso, S. Mayrargue, Subspace methods for the blind identification of multichannel FIR filters, ICASSP94 Adelaide, South Australia.1994,:-[8]P. Vandaele, M. Moonen, Adaptive algorithms for single user blind channel equalization, Proceedings of the Fourth IMA Conference on Mathematics in Signal Processing, Warwick, Dec. 1996,68-72.[9]羅來源,肖先賜,基于卡爾曼濾波技術(shù)的盲均衡算法,95年儀器儀表與信號處理年會文集,鄭州,1995,179-184.
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出版歷程
  • 收稿日期:  2001-11-30
  • 修回日期:  2002-10-14
  • 刊出日期:  2003-06-19

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