• DocumentCode
    3416488
  • Title

    Bayesian MLSD for multipath Rayleigh fading channels

  • Author

    Roufarshbaf, Hossein ; Nelson, Jill K.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., George Mason Univ., Fairfax, VA
  • fYear
    2008
  • fDate
    March 31 2008-April 4 2008
  • Firstpage
    2845
  • Lastpage
    2848
  • Abstract
    We propose a tree-search based Bayesian approach to blind maximum likelihood sequence detection (MLSD) of convolutionally encoded data transmitted over a multipath Rayleigh fading channel. In deriving the path metric for searching the channel-code tree, the proposed algorithm incorporates a forgetting factor matched to the time variation of the channel to generate accurate estimates of the correlation across the transmitted and received data. In addition, an augmented metric is presented to address the challenge of unknown channel order in time-varying systems. Simulation results show that the proposed algorithm can achieve significant improvement in bit error rate over competing schemes, even when channel order information is unavailable at the receiver.
  • Keywords
    Bayes methods; Rayleigh channels; convolutional codes; maximum likelihood detection; multipath channels; Bayesian approach; augmented metric; blind maximum likelihood sequence detection; channel code tree; convolutionally encoded data; forgetting factor; multipath Rayleigh fading channels; path metric; time varying system; tree search; unknown channel order; AWGN; Bandwidth; Bayesian methods; Channel estimation; Detectors; Fading; Intersymbol interference; Maximum likelihood detection; Maximum likelihood estimation; Time-varying channels; Maximum likelihood detection; equalizers; multipath channels; time-varying channels;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
  • Conference_Location
    Las Vegas, NV
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-1483-3
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2008.4518242
  • Filename
    4518242