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Estimating the list size for BEAST-APP decoding

Loncar, Maja LU ; Johannesson, Rolf LU ; Bocharova, Irina LU and Kudryashov, Boris LU (2005) IEEE International Symposium on Information Theory (ISIT), 2005 In 2005 IEEE International Symposium on Information Theory (ISIT) p.1126-1130
Abstract
The BEAST-APP decoding algorithm is a low-complexity bidirectional algorithm that searches code trees to find the list of the most likely codewords, which are used to compute approximate a posteriori probabilities (APPs) of the transmitted symbols. It can be applied to APP-decoding of any linear block code, as well as in iterative structures for decoding concatenated block codes. Previous work has shown that the list size sufficient to achieve the performance of true-APP decoding is very small. This paper aims at providing a theoretical justification for this result. The sufficient list size is estimated first via the minimum list distance - a parameter that is defined and analyzed as a key factor that governs the performance of list-based... (More)
The BEAST-APP decoding algorithm is a low-complexity bidirectional algorithm that searches code trees to find the list of the most likely codewords, which are used to compute approximate a posteriori probabilities (APPs) of the transmitted symbols. It can be applied to APP-decoding of any linear block code, as well as in iterative structures for decoding concatenated block codes. Previous work has shown that the list size sufficient to achieve the performance of true-APP decoding is very small. This paper aims at providing a theoretical justification for this result. The sufficient list size is estimated first via the minimum list distance - a parameter that is defined and analyzed as a key factor that governs the performance of list-based algorithms. Additionally, statistical properties of the codeword likelihoods are investigated and the typical list structure is presented. Preliminary simulation results for iterative BEAST decoding confirm the list-size estimates obtained from both approaches (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
BEAST-APP decoding, a posteriori probabilities, concatenated block codes, linear block code, iterative decoding, list-size estimates, code trees
in
2005 IEEE International Symposium on Information Theory (ISIT)
pages
1126 - 1130
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Symposium on Information Theory (ISIT), 2005
external identifiers
  • WOS:000234713800236
ISBN
0-7803-9150-0
DOI
10.1109/ISIT.2005.1523515
language
English
LU publication?
yes
id
595292e8-5adb-4e93-ad97-13113d6f1e66 (old id 616400)
date added to LUP
2007-11-25 15:12:43
date last changed
2016-07-11 12:49:37
@misc{595292e8-5adb-4e93-ad97-13113d6f1e66,
  abstract     = {The BEAST-APP decoding algorithm is a low-complexity bidirectional algorithm that searches code trees to find the list of the most likely codewords, which are used to compute approximate a posteriori probabilities (APPs) of the transmitted symbols. It can be applied to APP-decoding of any linear block code, as well as in iterative structures for decoding concatenated block codes. Previous work has shown that the list size sufficient to achieve the performance of true-APP decoding is very small. This paper aims at providing a theoretical justification for this result. The sufficient list size is estimated first via the minimum list distance - a parameter that is defined and analyzed as a key factor that governs the performance of list-based algorithms. Additionally, statistical properties of the codeword likelihoods are investigated and the typical list structure is presented. Preliminary simulation results for iterative BEAST decoding confirm the list-size estimates obtained from both approaches},
  author       = {Loncar, Maja and Johannesson, Rolf and Bocharova, Irina and Kudryashov, Boris},
  isbn         = {0-7803-9150-0},
  keyword      = {BEAST-APP decoding,a posteriori probabilities,concatenated block codes,linear block code,iterative decoding,list-size estimates,code trees},
  language     = {eng},
  pages        = {1126--1130},
  publisher    = {ARRAY(0x9b70520)},
  series       = {2005 IEEE International Symposium on Information Theory (ISIT)},
  title        = {Estimating the list size for BEAST-APP decoding},
  url          = {http://dx.doi.org/10.1109/ISIT.2005.1523515},
  year         = {2005},
}