A soft-decision iterative decoding algorithm using a top-down and recursive minimum distance search

Jun Asatani, Kenichi Tomita, Takuya Koumoto, Toyoo Takata, Tadao Kasami

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, we present a new soft-decision iterative decoding algorithm using an efficient minimum distance search (MDS) algorithm. The proposed MDS algorithm is a top-down and recursive MDS algorithm, which finds a most likely codeword among the codewords at the minimum distance of the code from a given codeword. A search is made in each divided section by a "call by need" from the upper section. As a consequence, the search space and computational complexity are reduced significantly. The simulation results show that the proposed decoding algorithm achieves near error performance to the maximum likelihood decoding for any RM code of length 128 and suboptimal for the (256, 37), (256, 93) and (256, 163) RM codes.

Original languageEnglish
Pages (from-to)2220-2228
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE85-A
Issue number10
Publication statusPublished - Oct 2002
Externally publishedYes

Keywords

  • Iterative decoding
  • Minimum distance search
  • Minimum weight codewords
  • Recursive maximum likelihood decoding
  • Reed-Muller code

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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