Adaptive Bayesian and EM-based detectors for frequency-selective fading channels

Mauri Nissilä, Subbarayan Pasupathy

Research output: Contribution to journalArticleScientificpeer-review

33 Citations (Scopus)


The problems of adaptive maximum a posteriori (MAP) symbol detection for uncoded transmission and of adaptive soft-input soft-output (SISO) demodulation for coded transmission of data symbols over time-varying frequency-selective channels are explored within the framework of the expectation-maximization (EM) algorithm. In particular, several recursive forms of the classical Baum-Welch (BW) algorithm and its Bayesian counterpart (often referred to a Bayesian EM algorithm) are derived in an unified way. In contrast to earlier developments of the BW and BEM algorithms, these formulations lead to computationally attractive algorithms which avoid matrix inversions while using sequential processing over the time and trellis branch indices. Moreover, it is shown how these recursive versions of the BW and BEM algorithms can be integrated with the well-known forward-backward processing SISO algorithms resulting in adaptive SISOs with embedded soft decision directed (SDD) channel estimators. An application of the proposed algorithms to iterative "turbo-processing" receivers illustrates how these SDD channel estimators can efficiently exploit the extrinsic information obtained as feedback from the SISO decoder in order to enhance their estimation accuracy.
Original languageEnglish
Pages (from-to)1325-1336
JournalIEEE Transactions on Communications
Issue number8
Publication statusPublished - 2003
MoE publication typeA1 Journal article-refereed


  • Bayes methods
  • bayesian
  • adaptive signal detection
  • channel estimation
  • demodulation
  • fading channels
  • iterative decoding
  • optimisation
  • statistical analysis
  • frequency-selective channels
  • iterative receivers
  • matrix inversions
  • recursive algorithms
  • soft statistics
  • time indices
  • trellis branch indices


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