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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
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2002/10/01
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)
Category: Coding Theory
minimum distance search, recursive maximum likelihood decoding, minimum weight codewords, Reed-Muller code, iterative decoding,
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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.