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

Mauri Nissilä, Subbarayan Pasupathy

Research output: Contribution to journalArticleScientificpeer-review

32 Citations (Scopus)

Abstract

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
Number of pages12
JournalIEEE Transactions on Communications
Volume51
Issue number8
DOIs
Publication statusPublished - 2003
MoE publication typeA1 Journal article-refereed

Fingerprint

Frequency selective fading
Fading channels
Detectors
Processing
Demodulation
Feedback

Keywords

  • 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

Cite this

Nissilä, Mauri ; Pasupathy, Subbarayan. / Adaptive Bayesian and EM-based detectors for frequency-selective fading channels. In: IEEE Transactions on Communications. 2003 ; Vol. 51, No. 8. pp. 1325-1336.
@article{188f3d0887eb4d31b0bf567f9b18b494,
title = "Adaptive Bayesian and EM-based detectors for frequency-selective fading channels",
abstract = "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.",
keywords = "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",
author = "Mauri Nissil{\"a} and Subbarayan Pasupathy",
year = "2003",
doi = "10.1109/TCOMM.2003.815050",
language = "English",
volume = "51",
pages = "1325--1336",
journal = "IEEE Transactions on Communications",
issn = "0090-6778",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
number = "8",

}

Adaptive Bayesian and EM-based detectors for frequency-selective fading channels. / Nissilä, Mauri; Pasupathy, Subbarayan.

In: IEEE Transactions on Communications, Vol. 51, No. 8, 2003, p. 1325-1336.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Adaptive Bayesian and EM-based detectors for frequency-selective fading channels

AU - Nissilä, Mauri

AU - Pasupathy, Subbarayan

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

KW - Bayes methods

KW - bayesian

KW - adaptive signal detection

KW - channel estimation

KW - demodulation

KW - fading channels

KW - iterative decoding

KW - optimisation

KW - statistical analysis

KW - frequency-selective channels

KW - iterative receivers

KW - matrix inversions

KW - recursive algorithms

KW - soft statistics

KW - time indices

KW - trellis branch indices

U2 - 10.1109/TCOMM.2003.815050

DO - 10.1109/TCOMM.2003.815050

M3 - Article

VL - 51

SP - 1325

EP - 1336

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

SN - 0090-6778

IS - 8

ER -