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
Propomos um novo algoritmo de decodificação chamado “decodificação de amostragem”, que é construído usando um método Markov Chain Monte Carlo (MCMC) e implementa a decodificação de Máxima Probabilidade Posterior de maneira aproximada. É também mostrado que a decodificação amostral pode ser facilmente estendida à codificação universal ou aplicável a fontes Markov. Em experimentos de simulação comparando o algoritmo proposto com o algoritmo de decodificação de produto de soma, a decodificação de amostragem apresenta melhor desempenho à medida que o tamanho da amostra aumenta, embora o tempo de decodificação se torne proporcionalmente mais longo. O tempo de mistura, que mede o tamanho da amostra necessário para que o processo MCMC convirja para a distribuição limite, é avaliado para uma construção simples de matriz de codificação.
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 -