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.

In this paper, we consider linear subcodes of RMr,m whose bases are formed from the monomial basis of RMr,m by deleting ΔK monomials of degree r where ΔK < . For such subcodes, a procedure for computing the number of minimum weight codewords is presented and it is shown how to delete ΔK monomials in order to obtain a subcode with the smallest number of codewords of the minimum weight. For ΔK 3, a formula for the number of codewords of the minimum weight is presented. A (64,40) subcode of RM3,6 is being considered as an inner code in a concatenated coding system for NASA's high-speed satellite communications. For (64,40) subcodes, there are three equivalent classes. For each class, the number of minimum weight codewords, that of the second smallest weight codewords and simulation results on error probabilities of soft-decision maximum likelihood decoding are presented.