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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.11 pp.1512-1523

- Publication Date
- 2019/11/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E102.A.1512

- Type of Manuscript
- PAPER

- Category
- Coding Theory

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 -