The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous proposons un nouvel algorithme de décodage appelé « décodage par échantillonnage », qui est construit à l'aide d'une méthode de Markov Chain Monte Carlo (MCMC) et implémente le décodage de probabilité maximale a posteriori de manière approximative. Il est également montré que le décodage par échantillonnage peut être facilement étendu au codage universel ou être applicable aux sources Markov. Dans des expériences de simulation comparant l'algorithme proposé à l'algorithme de décodage par produit somme, le décodage par échantillonnage s'avère plus performant à mesure que la taille de l'échantillon augmente, bien que le temps de décodage devienne proportionnellement plus long. Le temps de mélange, qui mesure la taille d'échantillon nécessaire pour que le processus MCMC converge vers la distribution limite, est évalué pour une construction simple de matrice de codage.
Shigeki MIYAKE
NTT Network Innovation Laboratories
Jun MURAMATSU
NTT Communication Science Laboratories
Takahiro YAMAGUCHI
NTT Network Innovation Laboratories
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Shigeki MIYAKE, Jun MURAMATSU, Takahiro YAMAGUCHI, "Decoding via Sampling" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 11, pp. 1512-1523, November 2019, doi: 10.1587/transfun.E102.A.1512.
Abstract: We propose a novel decoding algorithm called “sampling decoding”, which is constructed using a Markov Chain Monte Carlo (MCMC) method and implements Maximum a Posteriori Probability decoding in an approximate manner. It is also shown that sampling decoding can be easily extended to universal coding or to be applicable for Markov sources. In simulation experiments comparing the proposed algorithm with the sum-product decoding algorithm, sampling decoding is shown to perform better as sample size increases, although decoding time becomes proportionally longer. The mixing time, which measures how large a sample size is needed for the MCMC process to converge to the limiting distribution, is evaluated for a simple coding matrix construction.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1512/_p
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@ARTICLE{e102-a_11_1512,
author={Shigeki MIYAKE, Jun MURAMATSU, Takahiro YAMAGUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Decoding via Sampling},
year={2019},
volume={E102-A},
number={11},
pages={1512-1523},
abstract={We propose a novel decoding algorithm called “sampling decoding”, which is constructed using a Markov Chain Monte Carlo (MCMC) method and implements Maximum a Posteriori Probability decoding in an approximate manner. It is also shown that sampling decoding can be easily extended to universal coding or to be applicable for Markov sources. In simulation experiments comparing the proposed algorithm with the sum-product decoding algorithm, sampling decoding is shown to perform better as sample size increases, although decoding time becomes proportionally longer. The mixing time, which measures how large a sample size is needed for the MCMC process to converge to the limiting distribution, is evaluated for a simple coding matrix construction.},
keywords={},
doi={10.1587/transfun.E102.A.1512},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Decoding via Sampling
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1512
EP - 1523
AU - Shigeki MIYAKE
AU - Jun MURAMATSU
AU - Takahiro YAMAGUCHI
PY - 2019
DO - 10.1587/transfun.E102.A.1512
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2019
AB - We propose a novel decoding algorithm called “sampling decoding”, which is constructed using a Markov Chain Monte Carlo (MCMC) method and implements Maximum a Posteriori Probability decoding in an approximate manner. It is also shown that sampling decoding can be easily extended to universal coding or to be applicable for Markov sources. In simulation experiments comparing the proposed algorithm with the sum-product decoding algorithm, sampling decoding is shown to perform better as sample size increases, although decoding time becomes proportionally longer. The mixing time, which measures how large a sample size is needed for the MCMC process to converge to the limiting distribution, is evaluated for a simple coding matrix construction.
ER -