• DocumentCode
    2122929
  • Title

    Pre-whitened dithered signed-error constant modulus algorithm for efficient blind channel equalization

  • Author

    Butt, Naveed R. ; Cheded, L.

  • Author_Institution
    Dept. of Syst. Eng., King Fahd Univ. of Pet. & Miner., Dhahran
  • fYear
    0
  • fDate
    0-0 0
  • Lastpage
    6
  • Abstract
    Blind channel equalization has gained great importance in the world of communications. Among a large number of available blind equalization algorithms, the CMA (constant modulus algorithm) enjoys widespread popularity because of its LMS-like complexity and robustness. Two important improvements on the CMA performance are the dithered signed-error CMA (DSE-CMA) and the pre-whitened CMA (PW-CMA). The DSE-CMA is an approach to reduce the computational complexity of the CMA while retaining its robustness and the PW-CMA aims at improving the convergence rate of the CMA in case of channels exhibiting large frequency response deviations. In this paper we review the two approaches and propose a new scheme combining the virtues of the two. The combined scheme is computationally simpler than the PW-CMA and provides better convergence than the DSE-CMA. It is particularly suited for the situations where ill-convergence needs to be treated with minimum additional complexity and without loss of robustness
  • Keywords
    blind equalisers; computational complexity; convergence; blind channel equalization; computational complexity; constant modulus algorithm; convergence rate; dithered signed-error CMA; pre-whitened CMA; Adaptive equalizers; Blind equalizers; Coaxial cables; Computational complexity; Convergence; Filters; Minerals; Noise robustness; Optical distortion; Petroleum;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Control Applications, 2005. ICIECA 2005. International Conference on
  • Conference_Location
    Quito
  • Print_ISBN
    0-7803-9419-4
  • Type

    conf

  • DOI
    10.1109/ICIECA.2005.1644366
  • Filename
    1644366