We consider a soft-decision iterative bounded distance decoding algorithm for binary linear block codes. In the decoding algorithm, bounded distance decodings are carried out with respect to successive input words, called the search centers. A search center is the sum of the hard-decision sequence of a received sequence and a sequence in a set of test patterns which are generated beforehand. This set of test patterns has influence on the error performance of the decoding algorithms as simulation results show. In this paper, we propose a construction method of a set of candidate test patterns and a selection method of test patterns based on an introduced measure of effectiveness of test patterns. For several BCH codes of lengths 127, 255 and 511, we show the effectiveness of the proposed method by simulation.

For binary linear block codes, we introduce "multiple GMD decoding algorithm. " In this algorithm, GMD-like decoding is iterated around a few appropriately selected search centers. The original GMD decoding by Forney is a GMD-like decoding around the hard-decision sequence. Compared with the original GMD decoding, this decoding algorithm provides better error performance with moderate increment of iteration numbers. To reduce the number of iterations, we derive new effective sufficient conditions on the optimality of decoded codewords.

We consider iterative soft-decision decoding algorithms in which bounded distance decodings are carried out with respect to successively selected input words, called search centers. Their error performances are degraded by the decoding failure of bounded distance decoding and the duplication in generating candidate codewords. To avoid those weak points, we present a new method of selecting sequences of search centers. For some BCH codes, we show the effectiveness by simulation results.

In this paper, we consider sufficient conditions for ruling out some useless iteration steps in a class of soft-decision iterative decoding algorithms for binary block codes used over the AWGN channel using BPSK signaling. Sufficient conditions for ruling out the next single decoding step, called ruling-out conditions and those for ruling out all the subsequent iteration steps, called early termination conditions, are formulated in a unified way without degradation of error performance. These conditions are shown to be a type of integer programming problems. Several techniques for reducing such an integer programming problem to a set of subprograms with smaller computational complexities are presented. As an example, an early termination condition for Chase-type decoding algorithm is presented. Simulation results for the (64, 42, 8) Reed-Muller code and (64, 45, 8) extended BCH code show that the early termination condition combined with a ruling-out condition proposed previously is considerably effective in reducing the number of test error patterns, especially as the total number of test error patterns concerned grows.

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 an iterative decoding algorithm, such as Chase Type-II decoding algorithm and its improvements, candidate codewords for a received vector are generated for test based on a bounded-distance decoder and a set of test error patterns. It is desirable to remove useless test error patterns in these decoding algorithms. This paper presents a sufficient condition for ruling out some useless test error patterns. If this condition holds for a test error patterns e, then e can not produce a candidate codeword with a correlation metric larger than those of the candidate codewords generated already and hence e is useless. This significantly reduces the decoding operations in Chase type-II decoding algorithm or decoding iterations in its improvements.

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.

Subtrellises for low-weight codewords of binary linear block codes have been recently used in a number of trellis-based decoding algorithms to achieve near-optimum or suboptimum error performance with a significant reduction in decoding complexity. An algorithm for purging a full code trellis to obtain a low-weight subtrellis has been proposed by H.T. Moorthy et al. This algorithm is effective for codes of short to medium lengths, however for long codes, it becomes very time consuming. This paper investigates the structure and complexity of low-weight subtrellises for binary linear block codes. A construction method for these subtrellises is presented. The state and branch complexities of low-weight subtrellises for Reed-Muller codes and some extended BCH codes are given. In addition, a recursive algorithm for searching the most likely codeword in low-weight subtrellis-based decoding algorithm is proposed. This recursive algorithm is more efficient than the conventional Viterbi algorithm.