Wavelet Frequency Domain Weighted Multi-modulus Blind Equalization Algorithm Based on Lower Order Statistics

  • Jun Guo
  • Ye-cai Guo
  • Fang Xu
  • Qu Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 206)


The characteristics of multi-modulus signal and \( \alpha \)-stable distribution noise are analyzed, wavelet transform frequency domain weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSFWMMA) is proposed. The proposed algorithm first uses the fractional lower order statistics to suppress \( \alpha \)-stable distribution noise and uses the minimum dispersion coefficient criterion instead of the traditional least mean square error criterion to optimize the multi-modulus blind equalization algorithm, on the other hand, its computational loads can be reduced by using Fast Fourier Transform (FFT) technique and the overlapping retention law, and orthogonal wavelet transform is used to improve the convergence rate. The simulations in underwater acoustic channels show that the proposed algorithm has faster convergence speed and smaller steady state error, so it can be used in underwater acoustic communication.


Fractional lower order statistics α-stable distribution noise Weighted multi-modulus blind equalization Orthogonal wavelet Multi-modulus signal 



This paper is supported by Specialized Fund for the Author of National Excellent Doctoral Dissertation of China (200753), Natural Science Foundation of Higher Education Institution of Jiangsu Province (08KJB510010) and “the peak of six major talent” cultivate projects of Jiangsu Province (2008026), Natural Science Foundation of Jiangsu Province (BK2009410), Natural Science Foundation of Higher Education Institution of Anhui Province (2010A096), Postgraduate Research and Innovation projects of Higher Education Institution of Jiangsu Province (CXLX11_0637).


  1. 1.
    Han Y (2007) Blind equalizer design and algorithm simulation based on wavelet transform. vol 1(2), pp 98-104, Anhui University Of Science And Technology Google Scholar
  2. 2.
    Zhang JF, Qiu TS, Tang H (2007) Robustness analysis of DLMP algorithm under α-stable noise environment. Acta Electron 35(3):515–519Google Scholar
  3. 3.
    Li X (2006) Alpha stable distribution model and its application. Dr Diss Huazhong Univ Sci Technol 11(4):379–384Google Scholar
  4. 4.
    Zhao Z, Fu B, Shang J (2008) Adaptive Filter in Wavelet domain under Alpha-stable distribution pulse noise. 9th Int Conf Signal Process (ICSP2008) 3(48):211–214Google Scholar
  5. 5.
    Yang C (2009) Combined blind equalization algorithms based on wavelet transform. Master thesis, vol 13(44), pp 182-187 Anhui University Of Science And Technology Google Scholar
  6. 6.
    Shuyu F, Heng D (2007) An improved variable step frequency-domain block LMS adaptive filtering algorithm. Mod Electron Technol 24(1):144–146Google Scholar
  7. 7.
    Zhang Y, Zhao J, Guo Y, Li J (2011) Blind adaptive MMSE equalization of underwater acoustic channels based on the linear prediction method. J Marine Sci Appl 10:113–120Google Scholar
  8. 8.
    Baojun C (2007) The research of signal processing in Alpha stable distribution noise environments. Master thesis, vol 28(2), pp 203-206 Xidian UniversityGoogle Scholar
  9. 9.
    Zhao ZJ, Kehai D, Shang JN, Kong XZ (2009) Adaptive data block filtering algorithms for the α-stable distribution. J Circuits Syst 14(3):28–32Google Scholar
  10. 10.
    Zhang Y, Zhao J, Guo Y, Li J (2010) A improved constant modulus blind equalization algorithm of inhibition of α-stable noise. J Northwest Polytech Univ 28(2):203–206Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringNanjing University of Information Science and TechnologyNanjingChina

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