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Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference.

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Title: Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference.
Authors: Rong-Hui Peng1 peng@ece.utah.edu, Rong-Rong Chen1 rchen@ece.utah.edu, Farhang-Boroujeny, Behrouz1 farhang@ece.utah.edu
Source: IEEE Transactions on Signal Processing. Apr2010, Vol. 58 Issue 4, p2206-2217. 12p. 8 Graphs.
Subjects: Markov processes, Monte Carlo method, Detectors, Equalizers (Electronics), Electronics
Abstract: In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and the linear minimum mean square error (MMSE) equalizer significantly for channels with severe amplitude distortion. Direct application of MCMC to channel equalization requires sequential processing which leads to long processing delay. We develop a parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that both the sequential and parallel processing MCMC equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum maximum a posteriori (MAP) equalizer. The MAP equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed MCMC equalizers grows linearly. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Signal Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference.
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  Data: <searchLink fieldCode="AR" term="%22Rong-Hui+Peng%22">Rong-Hui Peng</searchLink><relatesTo>1</relatesTo><i> peng@ece.utah.edu</i><br /><searchLink fieldCode="AR" term="%22Rong-Rong+Chen%22">Rong-Rong Chen</searchLink><relatesTo>1</relatesTo><i> rchen@ece.utah.edu</i><br /><searchLink fieldCode="AR" term="%22Farhang-Boroujeny%2C+Behrouz%22">Farhang-Boroujeny, Behrouz</searchLink><relatesTo>1</relatesTo><i> farhang@ece.utah.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Signal+Processing%22">IEEE Transactions on Signal Processing</searchLink>. Apr2010, Vol. 58 Issue 4, p2206-2217. 12p. 8 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Equalizers+%28Electronics%29%22">Equalizers (Electronics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electronics%22">Electronics</searchLink>
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  Label: Abstract
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  Data: In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and the linear minimum mean square error (MMSE) equalizer significantly for channels with severe amplitude distortion. Direct application of MCMC to channel equalization requires sequential processing which leads to long processing delay. We develop a parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that both the sequential and parallel processing MCMC equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum maximum a posteriori (MAP) equalizer. The MAP equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed MCMC equalizers grows linearly. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IEEE Transactions on Signal Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1109/TSP.2009.2038958
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 2206
    Subjects:
      – SubjectFull: Markov processes
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Detectors
        Type: general
      – SubjectFull: Equalizers (Electronics)
        Type: general
      – SubjectFull: Electronics
        Type: general
    Titles:
      – TitleFull: Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference.
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            NameFull: Rong-Hui Peng
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            NameFull: Rong-Rong Chen
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            NameFull: Farhang-Boroujeny, Behrouz
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            – D: 01
              M: 04
              Text: Apr2010
              Type: published
              Y: 2010
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              Value: 58
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            – TitleFull: IEEE Transactions on Signal Processing
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