Joint channel estimation and decoding using Gaussian approximation in a factor graph over multipath channel

Yang Liu, Loïc Brunel, Joseph J. Boutros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Joint channel estimation and decoding using belief propagation on factor graphs requires the quantization of probability densities since continuous parameters are involved. We propose to replace these densities by standard messages where the channel estimate is accurately modeled as a Gaussian mixture over multipah channel. Upward messages include symbol extrinsic information and downward messages carry mean values and variances for the Gaussian modeled channel estimate. Such unquantized message propagation leads to a complexity reduction and a performance improvement. Over multipath channel, the proposed belief propagation almost achieves the performance of iterative APP equalizer and outperforms MMSE equalizer.

Original languageEnglish
Title of host publication2009 IEEE 20th Personal, Indoor and Mobile Radio Communications Symposium, PIMRC 2009
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 IEEE 20th Personal, Indoor and Mobile Radio Communications Symposium, PIMRC 2009 - Tokyo, Japan
Duration: 13 Sep 200916 Sep 2009

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Other

Other2009 IEEE 20th Personal, Indoor and Mobile Radio Communications Symposium, PIMRC 2009
CountryJapan
CityTokyo
Period13/9/0916/9/09

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Liu, Y., Brunel, L., & Boutros, J. J. (2009). Joint channel estimation and decoding using Gaussian approximation in a factor graph over multipath channel. In 2009 IEEE 20th Personal, Indoor and Mobile Radio Communications Symposium, PIMRC 2009 [5449857] (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC). https://doi.org/10.1109/PIMRC.2009.5449857