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Performance evaluation of list sequence MAP decoding

Leanderson, Carl Fredrik LU and Sundberg, CEW (2005) In IEEE Transactions on Communications 53(3). p.422-432
Abstract
List-sequence (LS) decoding has the potential to yield significant coding gain additional to that of conventional single-sequence decoding, and it can be implemented with full backward compatibility in systems where an error-detecting code is concatenated with an error-correcting code. LS maximum-likelihood (ML) decoding provides a list of estimated sequences in likelihood order. For convolutional codes, this list can be obtained with the serial list Viterbi algorithm (SLVA). Through modification of the metric increments of the SLVA, an LS maximum a posteriori (MAP) probability decoding algorithm is obtained that takes into account bitwise a priori probabilities and produces an ordered list of sequence MAP estimates. The performance of the... (More)
List-sequence (LS) decoding has the potential to yield significant coding gain additional to that of conventional single-sequence decoding, and it can be implemented with full backward compatibility in systems where an error-detecting code is concatenated with an error-correcting code. LS maximum-likelihood (ML) decoding provides a list of estimated sequences in likelihood order. For convolutional codes, this list can be obtained with the serial list Viterbi algorithm (SLVA). Through modification of the metric increments of the SLVA, an LS maximum a posteriori (MAP) probability decoding algorithm is obtained that takes into account bitwise a priori probabilities and produces an ordered list of sequence MAP estimates. The performance of the resulting LS-MAP decoding algorithm is studied in this paper. Computer simulations and approximate analytical expressions, based on geometrical considerations of the decision domains of LS decoders, are presented. We focus on the frame-error performance of LS-MAP decoding, with genie-assisted error detection, on the additive white Gaussian noise channel. It is concluded that LS-MAP decoding exploits a priori information more efficiently, in order to achieve performance improvements, than does conventional single-sequence MAP decoding. Interestingly, LS-MAP decoding can provide significant improvements at low signal-to-noise ratios, compared with LS-ML decoding. In this environment, it is furthermore observed that feedback convolutional codes offer performance improvements over their feedforward counterparts. Since LS-MAP decoding can be implemented in existing systems at a modest complexity increase, it should have a wide area of applications, such as joint source-channel decoding and other kinds of iterative decoding. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
list-sequence (LS) decoding, A priori information, convolutional codes, sequence maximum a posteriori (MAP) decoding
in
IEEE Transactions on Communications
volume
53
issue
3
pages
422 - 432
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000228095000010
  • scopus:17644419302
ISSN
0090-6778
DOI
10.1109/TCOMM.2005.843426
language
English
LU publication?
yes
id
ac4fd069-9720-4946-ad8e-cef7e70a8430 (old id 246705)
date added to LUP
2007-08-22 11:35:51
date last changed
2017-01-01 07:24:49
@article{ac4fd069-9720-4946-ad8e-cef7e70a8430,
  abstract     = {List-sequence (LS) decoding has the potential to yield significant coding gain additional to that of conventional single-sequence decoding, and it can be implemented with full backward compatibility in systems where an error-detecting code is concatenated with an error-correcting code. LS maximum-likelihood (ML) decoding provides a list of estimated sequences in likelihood order. For convolutional codes, this list can be obtained with the serial list Viterbi algorithm (SLVA). Through modification of the metric increments of the SLVA, an LS maximum a posteriori (MAP) probability decoding algorithm is obtained that takes into account bitwise a priori probabilities and produces an ordered list of sequence MAP estimates. The performance of the resulting LS-MAP decoding algorithm is studied in this paper. Computer simulations and approximate analytical expressions, based on geometrical considerations of the decision domains of LS decoders, are presented. We focus on the frame-error performance of LS-MAP decoding, with genie-assisted error detection, on the additive white Gaussian noise channel. It is concluded that LS-MAP decoding exploits a priori information more efficiently, in order to achieve performance improvements, than does conventional single-sequence MAP decoding. Interestingly, LS-MAP decoding can provide significant improvements at low signal-to-noise ratios, compared with LS-ML decoding. In this environment, it is furthermore observed that feedback convolutional codes offer performance improvements over their feedforward counterparts. Since LS-MAP decoding can be implemented in existing systems at a modest complexity increase, it should have a wide area of applications, such as joint source-channel decoding and other kinds of iterative decoding.},
  author       = {Leanderson, Carl Fredrik and Sundberg, CEW},
  issn         = {0090-6778},
  keyword      = {list-sequence (LS) decoding,A priori information,convolutional codes,sequence maximum a posteriori (MAP) decoding},
  language     = {eng},
  number       = {3},
  pages        = {422--432},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Communications},
  title        = {Performance evaluation of list sequence MAP decoding},
  url          = {http://dx.doi.org/10.1109/TCOMM.2005.843426},
  volume       = {53},
  year         = {2005},
}