IEEE Transactions on Information Theory

Asymptotic optimality of the GMD and Chase decoding algorithms

Yuansheng Tang, Toru Fujiwara, Tadao Kasami

Published Aug 1, 2002• 9 citations

Abstract

The generalized minimum distance (GMD) and Chase (1972) decoding algorithms are some of the most important suboptimum bounded distance decoding algorithms for binary linear block codes over an additive white Gaussian noise (AWGN) channel. We compute the limitation of the ratio between the probability of decoding error for the GMD or any one of the Chase decoding algorithms and that of the maximum-likelihood (ML) decoding when the signal-to-noise ratio (SNR) approaches infinity. If the minimum Hamming distance of the code is greater than 2, the limitation is shown to be equal to 1 and thus the GMD and Chase decoding algorithms are asymptotically optimum.

DOI: 10.1109/tit.2002.800484

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