IEICE TRANSACTIONS on Fundamentals
Decoding via Sampling
Summary :
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
Authors
Shigeki MIYAKE
NTT Network Innovation Laboratories
Jun MURAMATSU
NTT Communication Science Laboratories
Takahiro YAMAGUCHI
NTT Network Innovation Laboratories
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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
Copy
@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},}
Copy
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 -
English
