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arXiv:0807.3582 · IEEE Transactions on Information Theory

Error-Correction Capability of Column-Weight-Three LDPC Codes

Shashi Kiran Chilappagari, Bane Vasic

Published Apr 22, 2009• 38 citations
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Abstract

In this paper, the error-correction capability of column-weight-three low-density parity-check (LDPC) codes when decoded using the Gallager A algorithm is investigated. It is proved that a necessary condition for a code to correct all error patterns with up to k ges 5 errors is to avoid cycles of length up to 2k in its Tanner graph. As a consequence of this result, it is shown that given any alpha > 0, exist N such that forall n > N, no code in the ensemble of column-weight-three codes can correct all alphan or fewer errors. The results are extended to the bit flipping algorithms.

DOI
10.1109/tit.2009.2015990

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Ever since the publication of Shannon's seminal work in 1948, the search for capacity achieving codes has led to many interesting discoveries in channel coding theory. Low-density parity-check (LDPC) codes originally proposed in 1963 were largely forgotten and rediscovered recently. The significance of LDPC codes lies in their capacity approaching performance even when decoded using low complexity sub-optimal decoding algorithms. Iterative decoders are one such class of decoders that work on a graphical representation of a code known as the Tanner graph. Their properties have been well understood in the asymptotic limit of the code length going to infinity. However, the behavior of various decoders for a given finite length code remains largely unknown.An understanding of the failures of the decoders is vital for the error floor analysis of a given code. Broadly speaking, error floor is the abrupt degradation in the frame error rate (FER) performance of a code in the high signal-to-noise ratio domain. Since the error floor phenomenon manifests in the regions not reachable by Monte-Carlo simulations, analytical methods are necessary for characterizing the decoding failures. In this work, we consider hard decision decoders for transmission over the binary symmetric channel (BSC).For column-weight-three codes, we provide tight upper and lower bounds on the guaranteed error correction capability of a code under the Gallager A algorithm by studying combinatorial objects known as trapping sets. For higher column weight codes, we establish bounds on the minimum number of variable nodes that achieve certain expansion as a function of the girth of the underlying Tanner graph, thereby obtaining lower bounds on the guaranteed error correction capability. We explore the relationship between a class of graphs known as cage graphs and trapping sets to establish upper bounds on the error correction capability.We also propose an algorithm to identify the most probable noise configurations, also known as instantons, that lead to error floor for linear programming (LP) decoding over the BSC. With the insight gained from the above analysis techniques, we propose novel code construction techniques that result in codes with superior error floor performance.

•Jan 1, 2008•0 citations
In this paper, the error-correction capability of column-weight-three low-density parity-check (LDPC) codes when decoded using the Gallager A algorithm is investigated. It is proved that a necessary condition for a code to correct all error patterns with up to k ges 5 errors is to avoid cycles of length up to 2k in its Tanner graph. As a consequence of this result, it is shown that given any alpha > 0, exist N such that forall n > N, no code in the ensemble of column-weight-three codes can correct all alphan or fewer errors. The results are extended to the bit flipping algorithms.
In this paper, we investigate the error correction capability of column-weight-three LDPC codes when decoded using the Gallager A algorithm. We prove that the necessary condition for a code to correct k ≥ 5 errors is to avoid cycles of length up to 2k in its Tanner graph. As a consequence of this result, we show that given any α> 0, ∃N such that ∀n> N, no code in the ensemble of column-weight-three codes can correct all αn or fewer errors. We extend these results to the bit flipping algorithm.
In this paper, we investigate the error correction capability of column-weight-three LDPC codes when decoded using the Gallager A algorithm. We prove that the necessary condition for a code to correct k≥5k \geq 5k≥5 errors is to avoid cycles of length up to 2k2k2k in its Tanner graph. As a consequence of this result, we show that given any $α>0, \exists N $ such that ∀n>N\forall n>N∀n>N, no code in the ensemble of column-weight-three codes can correct all αnαnαn or fewer errors. We extend these results to the bit flipping algorithm.
The error correction capability of bit-flipping decoding algorithm for low density parity-check (LDPC) codes is studied by introducing variable node adjacency (VNA) graphs which are derived from Tanner graphs of LDPC codes. For codes with column weight λ and girth g = 8, it can be shown that error patterns of weight less than or equal to λ - 1 can be corrected. This result implies that the bit-flipping algorithm could decode up to the random error-correcting capability over binary symmetric channel for girth 8 codes whose random error-correcting capability is equal to λ - 1.
The relation between the girth and the error correction capability of column-weight-three LDPC codes under the Gallager A algorithm is investigated. It is shown that a column-weight-three LDPC code with Tanner graph of girthg¿ 10 can correct all error patterns with up to(g/2-1) errors in at mostg/2 iterations of the Gallager A algorithm. For codes with Tanner graphs of girthg¿ 8, it is shown that girth alone cannot guarantee correction of all error patterns with up to(g/2-1) errors under the Gallager A algorithm. Sufficient conditions to correct(g/2-1) errors are then established by studying trapping sets.
We investigate the relation between the girth and the guaranteed error correction capability of γ-left regular LDPC codes. For column-weight-three codes, we give upper and lower bounds on the number of errors correctable by the Gallager A algorithm. For higher column weight codes, we find the number of variable nodes which are guaranteed to expand by a factor of at least 3γ/4, hence giving a lower bound on the guaranteed correction capability under the bit flipping (serial and parallel) algorithms. We also establish upper bounds by studying the sizes of smallest possible trapping sets.
Ever since the publication of Shannon's seminal work in 1948, the search for capacity achieving codes has led to many interesting discoveries in channel coding theory. Low-density parity-check (LDPC) codes originally proposed in 1963 were largely forgotten and rediscovered recently. The significance of LDPC codes lies in their capacity approaching performance even when decoded using low complexity sub-optimal decoding algorithms. Iterative decoders are one such class of decoders that work on a graphical representation of a code known as the Tanner graph. Their properties have been well understood in the asymptotic limit of the code length going to infinity. However, the behavior of various decoders for a given finite length code remains largely unknown.An understanding of the failures of the decoders is vital for the error floor analysis of a given code. Broadly speaking, error floor is the abrupt degradation in the frame error rate (FER) performance of a code in the high signal-to-noise ratio domain. Since the error floor phenomenon manifests in the regions not reachable by Monte-Carlo simulations, analytical methods are necessary for characterizing the decoding failures. In this work, we consider hard decision decoders for transmission over the binary symmetric channel (BSC).For column-weight-three codes, we provide tight upper and lower bounds on the guaranteed error correction capability of a code under the Gallager A algorithm by studying combinatorial objects known as trapping sets. For higher column weight codes, we establish bounds on the minimum number of variable nodes that achieve certain expansion as a function of the girth of the underlying Tanner graph, thereby obtaining lower bounds on the guaranteed error correction capability. We explore the relationship between a class of graphs known as cage graphs and trapping sets to establish upper bounds on the error correction capability.We also propose an algorithm to identify the most probable noise configurations, also known as instantons, that lead to error floor for linear programming (LP) decoding over the BSC. With the insight gained from the above analysis techniques, we propose novel code construction techniques that result in codes with superior error floor performance.