Bias-based training for iterative channel estimation and data decoding in fast fading channels

Keigo Takeuchi, Ralf R. Müller, Mikko Vehkaperä

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

A novel signaling scheme is proposed for iterative channel estimation and data decoding in fast fading channels. The basic idea is to bias the occurrence probability of transmitted symbols. A priori information about the bias is utilized for channel estimation. The bias-based scheme is constructed as a serially concatenated code, in which a convolutional code and a biased nonlinear block code are used as the outer and inner codes, respectively. This construction allows the receiver to estimate channel state information (CSI) efficiently. The proposed scheme is numerically shown to outperform conventional pilot-based schemes in terms of spectral efficiency for moderately fast fading channels.
Original languageEnglish
Pages (from-to)2161-2165
Number of pages5
JournalIEICE Transactions on Communications
VolumeE94-B
Issue number7
DOIs
Publication statusPublished - Jul 2011
MoE publication typeA1 Journal article-refereed

Keywords

  • Belief propagation
  • Bias-based channel estimation
  • Fast fading channels
  • Iterative decoding

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