Reduced Complexity Iterative Decoding Using a Sub-Optimum Minimum Distance Search

Jun Asatani; Takuya Koumoto; Kenichi Tomita; Tadao Kasami

Reduced Complexity Iterative Decoding Using a Sub-Optimum Minimum Distance Search

Jun Asatani, Takuya Koumoto, Kenichi Tomita, Tadao Kasami

Research output: Contribution to journal › Article › peer-review

Abstract

In this letter, we propose (1) a new sub-optimum minimum distance search (sub-MDS), whose search complexity is reduced considerably compared with optimum MDSs and (2) a termination criterion, called near optimality condition, to reduce the average number of decoding iterations with little degradation of error performance for the proposed decoding using sub-MDS iteratively. Consequently, the decoding algorithm can be applied to longer codes with feasible complexity. Simulation results for several Reed-Muller (RM) codes of lengths 256 and 512 are given.

Original language	English
Pages (from-to)	2596-2600
Number of pages	5
Journal	IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume	E86-A
Issue number	10
Publication status	Published - Oct 2003
Externally published	Yes

Keywords

Iterative decoding
Minimum distance search
Near optimality condition
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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N2 - In this letter, we propose (1) a new sub-optimum minimum distance search (sub-MDS), whose search complexity is reduced considerably compared with optimum MDSs and (2) a termination criterion, called near optimality condition, to reduce the average number of decoding iterations with little degradation of error performance for the proposed decoding using sub-MDS iteratively. Consequently, the decoding algorithm can be applied to longer codes with feasible complexity. Simulation results for several Reed-Muller (RM) codes of lengths 256 and 512 are given.

AB - In this letter, we propose (1) a new sub-optimum minimum distance search (sub-MDS), whose search complexity is reduced considerably compared with optimum MDSs and (2) a termination criterion, called near optimality condition, to reduce the average number of decoding iterations with little degradation of error performance for the proposed decoding using sub-MDS iteratively. Consequently, the decoding algorithm can be applied to longer codes with feasible complexity. Simulation results for several Reed-Muller (RM) codes of lengths 256 and 512 are given.

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KW - Minimum distance search

KW - Near optimality condition

KW - Reed-Muller code

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Reduced Complexity Iterative Decoding Using a Sub-Optimum Minimum Distance Search

Abstract

Keywords

ASJC Scopus subject areas

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