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IEEE Information Theory Workshop (ITW)

Erasure-Burst and Error-Burst Decoding of Linear Codes

Shumei Song, Shu Lin, Khaled Abdel-Ghaffar, Wai Fong

Published Sep 1, 2007• 6 citations
Publisher

Abstract

Iterative algorithms for decoding over erasure-burst channels have attracted much interest lately due to their simplicity. Although the performance of these techniques is typically good, it may not be optimal if the underlying matrix used for decoding is not properly chosen. In this paper, we construct matrices that lead to optimal performance when used with iterative decoding over erasure-burst channels. We also develop a simple decoding technique that makes use of these same matrices to decode any linear code over error-burst channels. For cyclic codes, this algorithm is optimal.

DOI
10.1109/itw.2007.4313062

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arXiv:0708.0905•IEEE Transactions on Information Theory•Dec 1, 2008•33 citations
Iterative algorithms for decoding over erasure-burst channels have attracted much interest lately due to their simplicity. Although the performance of these techniques is typically good, it may not be optimal if the underlying matrix used for decoding is not properly chosen. In this paper, we construct matrices that lead to optimal performance when used with iterative decoding over erasure-burst channels. We also develop a simple decoding technique that makes use of these same matrices to decode any linear code over error-burst channels. For cyclic codes, this algorithm is optimal.
LDPC codes have excellent performance when iteratively decoded based on sparse parity-check matrices over both the AWGN channel and the erasure channel. In this paper, we propose a simple burst-correction decoding scheme for cyclic LDPC codes based on the same sparse circulant parity-check matrices used to perform iterative decoding. Although the proposed scheme may not achieve the full burst-correcting capability of the codes, it is rather simple and fast. We also show that the full burst-correcting capabilities of some codes constructed from Euclidean and projective geometries are very close to the Reiger upper bound
Abstract — We introduce the notion of the stopping redundancy hierarchy of a linear block code as a measure of the trade-off between performance and complexity of iterative decoding for the binary erasure channel. We derive lower and upper bounds for the stopping redundancy hierarchy via Lovász’s Local Lemma and Bonferroni-type inequalities, and specialize them for codes with cyclic parity-check matrices. Based on the observed prop-erties of parity-check matrices with good stopping redundancy characteristics, we develop a novel decoding technique, termed automorphism group decoding, that combines iterative message passing and permutation decoding. We also present bounds on the smallest number of permutations of an automorphism group decoder needed to correct any set of erasures up to a prescribed size. Simulation results demonstrate that for a large number of algebraic codes, the performance of the new decoding method is close to that of maximum likelihood decoding.
Introduction: In modern systems for communication, data storage and processing the error-correction capability of codes are estimated for memoryless channels. In real channels the noise is correlated, which leads to grouping error in bursts. A traditional method to fight this phenomenon is channel decorrelation, which does not allow developing of coding schemes, mostly utilizing the channel capacity. Thus the development of bursts decoding algorithms for arbitrary linear codes is the actual task. Purpose: To develop a single error burst decoding algorithm for linear codes, to estimate the decoding error probability and computational complexity. Results: Two approaches are proposed to burst error correction. The first one is based on combining the window sliding modification of well-known bit-flipping algorithm with preliminary analysis of the structure of parity check matrix. The second one is based on the recursive procedure of constructing the sequence of certain events which, in the worst case, performs the exhaustive search of error bursts, but in many cases the search may be significantly decreased by using the proposed heuristics. The proposed recursive decoding algorithm allows a guaranteed correction of any single error bursts within burst-correction capability of the code, and in many cases beyond the burst-correction capability. The complexity of this algorithm is significantly lower than that of a bit flipping algorithm if the parity-check matrix of the code is sparse enough. An alternative hybrid decoding algorithm is proposed utilizing the bit-flipping approach and showing the error probability and completion time comparable to the recursive algorithm, however, in this case the possibility of a guaranteed burst correction hardly can be proved. Practical relevance: The proposed decoding methods may be used in modern and perspective communication systems, allowing energy saving and increasing reliability of data transmission by better error performance and computational complexity.
Most decoding algorithms of linear codes, in general, are designed to correct or detect errors. However, many channels cause erasures in addition to errors. In principle, decoding over such channels can be accomplished by deleting the erased symbols and decoding the resulting vector with respect to a punctured code. For any given linear code and any given maximum number of correctable erasures, we introduce parity-check matrices yielding parity-check equations that do not check any of the erased symbols and which are sufficient to characterize all punctured codes corresponding to this maximum number of erasures. This allows for the separation of erasures from errors to facilitate decoding. The parity-check matrices typically have redundant rows. We give several constructions of such matrices and prove general bounds on their minimum sizes.
The generalized iterative decoding concept offers attractive performance versus complexity trade-off opportunities in the spectrum between traditional iterative decoding and optimal decoding for linear block codes over the binary erasure channel. In each iteration, a system of equations is solved. The maximum number of equations to be solved in one iteration is called the order of the decoder. In case the order is just one, the generalized iterative decoder reduces to the traditional iterative decoder. On the other hand, if the order is set to the redundancy of the codes, the generalized iterative decoder gives the same performance as the optimal decoder. Varying the order between these two extremes allows for a better match to the system specifications. In this paper, we consider aspects regarding the implementation of generalized iterative decoding and we determine the minimum order (as a function of the girth) that can potentially lead to improvement over traditional iterative decoding.
We introduce the notion of the stopping redundancy hierarchy of a linear block code as a measure of the tradeoff between performance and complexity of iterative decoding for the binary erasure channel. We derive lower and upper bounds for the stopping redundancy hierarchy via Lovasz's local lemma (LLL) and Bonferroni-type inequalities, and specialize them for codes with cyclic parity-check matrices. Based on the observed properties of parity-check matrices with good stopping redundancy characteristics, we develop a novel decoding technique, termed automorphism group decoding, that combines iterative message passing and permutation decoding. We also present bounds on the smallest number of permutations of an automorphism group decoder needed to correct any set of erasures up to a prescribed size. Simulation results demonstrate that for a large number of algebraic codes, the performance of the new decoding method is close to that of maximum-likelihood (ML) decoding.