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IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

Sufficient Conditions for Ruling-Out Useless Iterative Steps in a Class of Iterative Decoding Algorithms

Tadao Kasami, Yuansheng Tang, Takuya Koumoto, Toru Fujiwara

Published Oct 25, 1999• 16 citations
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Abstract

SUMMARY In this paper, we consider sufficient conditions for ruling out some useless iteration steps in a class of softdecision 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.

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For iterative soft-decision decoding algorithms for binary linear block codes, error performance and computational complexity are very important factors.In this thesis, suÆcient conditions of the optimality of decoded codeword and a suÆcient condition which is used for ruling-out some useless iterations of bounded distance decoding which reduces the computational complexity of iterative soft-decision decoding algorithms are shown.New iterative soft-decision decoding algorithms which uses the suÆcient conditions of the optimality of decoded codeword to achievegood error performance with small computational complexity are also shown.First, the suÆcient conditions are derived based on: (1) partial knowledge of the distance pro le of the code, and (2) previously generated candidate codewords.When the numberof previously generated candidate codewords is greater than 1, the conditions presented here are less stringent than those derived by Taipale and Pursley [4] and Kaneko et.al.[5].The condition based on three previously generated candidate codewords is the rst such s u Æ c i e n t condition that has ever been derived.It is shown by computer simulation that these suÆcient condions reduce the computational complexity of iterative decoding algorithms without degradation of error performance.Second, a suÆcient condition to rule-out some useless test error patterns is derived.In an iterative decoding algorithm, such as Chase Type-II decoding algorithm[2] and its improved versions, candidate codewords for a received vector are generated

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A low-weight trellis-based iterative soft-decision decoding algorithm for binary linear block codes

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This paper presents a new low-weight trellis-based soft-decision iterative decoding algorithm for binary linear block codes. The algorithm is devised based on a set of optimality conditions and the generation of a sequence of candidate codewords for an optimality test. The initial candidate codeword is generated by a simple decoding method. The subsequent candidate codewords, if needed, are generated by a chain of low-weight trellis searches, one at a time. Each search is conducted through a low-weight trellis diagram centered around the latest candidate codeword and results in an improvement over the previous candidate codewords that have been already tested. This improvement is then used as the next candidate codeword for a test of optimality. The decoding iteration stops whenever a candidate codeword is found to satisfy a sufficient condition on optimality or the latest low-weight trellis search results in a repetition of a previously generated candidate codeword. A divide-and-conquer technique is also presented for codes that are not spanned by their minimum-weight codewords. The proposed decoding algorithm has been applied to some well-known codes of lengths 48, 64, and 128. Simulation results show that the proposed algorithm achieves either practically optimal error performance for the example codes of length 48 and 64 or near optimal error performance for the (128, 29, 32) RM code with a significant reduction in computational decoding complexity.

IEEE Transactions on Information Theory•Mar 1, 1999•13 citations
SUMMARY In this paper, we consider sufficient conditions for ruling out some useless iteration steps in a class of softdecision 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.
For iterative soft-decision decoding algorithms for binary linear block codes, error performance and computational complexity are very important factors.In this thesis, suÆcient conditions of the optimality of decoded codeword and a suÆcient condition which is used for ruling-out some useless iterations of bounded distance decoding which reduces the computational complexity of iterative soft-decision decoding algorithms are shown.New iterative soft-decision decoding algorithms which uses the suÆcient conditions of the optimality of decoded codeword to achievegood error performance with small computational complexity are also shown.First, the suÆcient conditions are derived based on: (1) partial knowledge of the distance pro le of the code, and (2) previously generated candidate codewords.When the numberof previously generated candidate codewords is greater than 1, the conditions presented here are less stringent than those derived by Taipale and Pursley [4] and Kaneko et.al.[5].The condition based on three previously generated candidate codewords is the rst such s u Æ c i e n t condition that has ever been derived.It is shown by computer simulation that these suÆcient condions reduce the computational complexity of iterative decoding algorithms without degradation of error performance.Second, a suÆcient condition to rule-out some useless test error patterns is derived.In an iterative decoding algorithm, such as Chase Type-II decoding algorithm[2] and its improved versions, candidate codewords for a received vector are generated
We consider an iterative decoding algorithm by using algebraic decoding for non-binary codes. In the decoding algorithm, algebraic decoding are carried out with respect to successively chosen input words, called the search centers. We present a new method for selecting sequences of search centers of algebraic decoding. Over the AWGN channel using BPSK signaling, we show simulation results on the error performances of the iterative decoding algorithms whose search centers are given by the proposed method, abbreviated IAD, for Reed-Solomon codes. From the simulation results, IAD provides considerably better error performance than GMD and CGA decoding with the same number of iterations.
In order to detect uncorrectable error frames and early terminate their iterative belief-propagation (BP) decoding for binary or nonbinary LDPC codes, a unified early stopping criterion using check-sum variation patterns is proposed. In addition to low computational overhead and easy choice of threshold parameters, it can be adapted to different channels. Simulation results show that the proposed criterion greatly reduces the average number of iterations (ANI) at low to medium signal-to-noise ratio (SNR) while keeping the error performance degradation negligible over a wide SNR region.
Iterative decoding methods have gained interest, initiated by the results of the so-called "turbo" codes. The theoretical description of this decoding, however, seems to be difficult. Therefore, we study the iterative decoding of block codes. First, we discuss the iterative decoding algorithms developed by Gallager (1962), Battail et al. (1979), and Hagenauer et al. (1996). Based on their results, we propose a decoding algorithm which only uses parity check vectors of minimum weight. We give the relation of this iterative decoding to one-step majority-logic decoding, and interpret it as gradient optimization. It is shown that the used parity check set defines the region where the iterative decoding decides on a particular codeword. We make plausible that, in almost all cases, the iterative decoding converges to a codeword after some iterations. We derive a computationally efficient implementation using the minimal trellis representing the used parity check set. Simulations illustrate that our algorithm gives results close to soft decision maximum likelihood (SDML) decoding for many code classes like BCH codes. Reed-Muller codes, quadratic residue codes, double circulant codes, and cyclic finite geometry codes. We also present simulation results for product codes and parallel concatenated codes based on block codes.
In conventional satellite broadcasting systems,e.g.DVB-S2,the error-correction schemes are often based on the concatenation of BCH and low-density parity-check(LDPC) codes,so as to obtain excellent bit error rate(BER) performance.As the inner codes,LDPC codes are traditionally decoded by iterative algorithms with many iteration cycles.In virtue of the concatenated structure of BCH and LDPC codes,a novel iterative decoding structure involving the embedment of BCH error-check function into each iterative cycle of LDPC is proposed.Computer simulations show that the iteration number of LDPC decoding with the proposed algorithm is obviously smaller than with traditional algorithm,thus greatly lowering the additional complexity of BCH error-check codes.Therefore,the total complexity and latency of the iterative decoding could be sufficiently reduced,while the similar system BER performance is maintained.
This paper presents a new low-weight trellis-based soft-decision iterative decoding algorithm for binary linear block codes. The algorithm is devised based on a set of optimality conditions and the generation of a sequence of candidate codewords for an optimality test. The initial candidate codeword is generated by a simple decoding method. The subsequent candidate codewords, if needed, are generated by a chain of low-weight trellis searches, one at a time. Each search is conducted through a low-weight trellis diagram centered around the latest candidate codeword and results in an improvement over the previous candidate codewords that have been already tested. This improvement is then used as the next candidate codeword for a test of optimality. The decoding iteration stops whenever a candidate codeword is found to satisfy a sufficient condition on optimality or the latest low-weight trellis search results in a repetition of a previously generated candidate codeword. A divide-and-conquer technique is also presented for codes that are not spanned by their minimum-weight codewords. The proposed decoding algorithm has been applied to some well-known codes of lengths 48, 64, and 128. Simulation results show that the proposed algorithm achieves either practically optimal error performance for the example codes of length 48 and 64 or near optimal error performance for the (128, 29, 32) RM code with a significant reduction in computational decoding complexity.