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.

An algorithm for finding the optimal sectionalization for sectionalized trellises with respect to distinct optimality criterions was presented by Lafourcade and Vardy. In this paper, for linear block codes, we give a direct method for finding the optimal sectionalization when the optimality criterion is chosen as the total number |E| of the edges, the expansion index |E|-|V|+1, or the quantity 2|E|-|V|+1, only using the dimensions of the past and future sub-codes. A more concrete method for determining the optimal sectionalization is given for the Reed-Muller codes with the natural lexicographic coordinate ordering.

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.