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DFT/LMS算法在DSSS中的应用及性能分析 被引量:2

The Application and Performance Analysis of the DFT/LMS algorithm In DSSS
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摘要 本文分析了直接序列扩频(DSSS)系统中最小错误概率(MPE)意义下的最优滤波器,并依据矩阵求逆引理证明最小均方误差(MMSE)意义下的最优滤波——维纳滤波也是MPE意义下的最优滤波。在DSSS中应用自适应滤波,无须先验已知扩频码的码型和干扰的统计特性,就能一并完成解扩以及有效抑制干扰。离散傅立叶变换/最小均方(DFT/LMS)算法的收敛速度远快于LMS算法,而运算量、稳健性与LMS算法基本相同。基于DFT/LMS算法的自适应滤波大大简化DSSS系统接收机的设计,显著增强系统抗干扰能力,具有很强的实用性。 This paper analyzes the optimum filter optimized in the minimum probabil-ty of erro (MPE) sense in direct sequence spread spectrum (DSSS), then proves that, using matrix inversion lemma, the Wiener filter optimized in the minimum mean square error (MMSE) sense is also the optimum filter in the MPE sense. Applying the adaptive filter in DSSS, despreading and suppressing interfrence can be done simultaneously without prior knowledge of the pseudonoise code and the statistical characterization of the interference. The discret fourier transform / least mean square (DFT/LMS) algorithm has significantly improved convergence speed over the least mean square (LMS) algorithm, and meanwhile the complexities and robust performance of the two algorithms are almost identical. Since the adaptive filter using the DFT/LMS algorithm significantly simplifies the design of the receiver, and notably enhances the capability of the anti-interference, it is of good practicability.
出处 《信号处理》 CSCD 2004年第3期322-325,289,共5页 Journal of Signal Processing
基金 JS63空间微波技术国防科技重点室基金(项目编号:2000JS63.3.1.KG0111)
关键词 DSSS DFT算法 LMS算法 性能分析 直接序列扩频系统 direct sequence spread spectrum(DSSS) minimum probability of error(MPE) interference suppression discret fourier transform/least mean square (DFT/LMS) algorithm
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参考文献7

  • 1H. Poor, Active interference suppression in CDMA overlay systems,IEEE J. Select. Areas Commun.,Vol.19,pp.4-20, Jan. 2001.
  • 2S.Haykin,Adaptive Filter Theory, Third Edition, Prentice-Hall,Inc,1996.
  • 3Vijay K.Madisetti,Douglas B.Williams,The Digital Signal Processing Handbook, 1998 by CRC Press LLC.
  • 4EBeaufays,Two-layer linear structures for fast adaptive filtering, Ph.D. diss., Information Systems Lab., Stanford University, Stanford, CA,1994,June 1995.
  • 5J.C.Lee, C.ICUn,Pefformance of Transform-Domain LMS Adaptive Digital Filters, IEEE Trans. Acoustics,Speech, Signal Processing,Vol.ASSP-34, 499-510,Jun.1986.
  • 6B.Widrow,E.Walach,Adaptive Inverse Control.刘树棠,韩崇昭译,西安交通大学出版社.2000.5.
  • 7M.J. Medley, Adaptive Narrow-Band Interference Suppression Using Linear Trans- forms and Multirate Filter Banks, Ph.D. thesis, Rensselaer Polytechnic Institute,Dec 1995.

同被引文献10

  • 1[1]S Haykin著,郑宝玉等译.自适应滤波器原理(第四版)[M].2003,北京:电子工业出版社.
  • 2[2]H S Punjabi, J K Townsend., A Duel-Hallen. A modified μ -weighted normalized frequency-domain LMS algorithm [C]. IEEE Global Telecommunications Conference, 1994.
  • 3[3]B Farhang-Boroujeny, S Gazor. Performance analysis of transform domain normalized LMS algorithm [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1991.
  • 4BUZZI S,LOPS M,POOR H V.Code-aided interference suppression for DS/CDMA overlay systems[J].Proceedings of IEEE,2002,90(3):394-435.
  • 5AROMAA S,HENTTU P,JUNTTI M.Transform-selective interference suppression algorithm for spread-spectrum communications[J].IEEE Signal Processing Letters,2005,12(1):49-51.
  • 6POOR H V,WANG X D.Code-aided interference suppression for DS/CDMA communications Part I:interference suppression capability[J].IEEE Transactions on Communications,1997,45(9):1101-1111.
  • 7CHOI J.MMSE equalization of downlink CDMA channel utilizing unused orthogonal spreading sequences[J].IEEE Transactions on Signal Processing,2003,51 (5):1390 -1402.
  • 8SIMON H.Adaptive filter theory (fourth edition)[M].NJ:Upper Saddle River,2003.
  • 9SONG H K.A channel estimation using sliding window approach and tuning algorithm for MLSE[J].IEEE Communications Letters,1999,3(7):211-213.
  • 10薛巍,向敬成,黄怀信.基于门限估计的直扩通信系统窄带干扰变换域抑制方法[J].电子与信息学报,2003,25(7):990-994. 被引量:25

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