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Decoding via Sampling

Shigeki MIYAKE, Jun MURAMATSU, Takahiro YAMAGUCHI

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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

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