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International Symposium on Information Theory and its Applications

Further Results on the Error Correction Capability of Irregular LDPC Codes under the Gallager A Algorithm

Masanori Hirotomo, Hiroto Tamiya, Masakatu Mori

Published Oct 1, 2018• 0 citations
Publisher

Abstract

In this paper, we investigate the error correction capability of irregular LDPC codes. We have shown relationships between the decoding failure and the structures of the Tanner graph of irregular LDPC codes under the Gallager A algorithm. In this analysis, we focused on the structures of connecting degree-two variable nodes, called 2-cycle. In this paper, we present new structures of the subgraph which induces the decoding failure of irregular LDPC codes under the Gallager A algorithm. Furthermore, we analyze relationships between the newly found structures of subgraph and the decoding failure of irregular LDPC codes.

DOI
10.23919/isita.2018.8664386

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In this paper, we investigate the error correction capability of irregular LDPC codes. We have shown relationships between the decoding failure and the structures of the Tanner graph of irregular LDPC codes under the Gallager A algorithm. In this analysis, we focused on the structures of connecting degree-two variable nodes, called 2-cycle. In this paper, we present new structures of the subgraph which induces the decoding failure of irregular LDPC codes under the Gallager A algorithm. Furthermore, we analyze relationships between the newly found structures of subgraph and the decoding failure of irregular LDPC codes.
Many researchers have analyzed the error correction capability of LDPC codes for iterative decoding algorithms. However, it is difficult to show the error correction capability of LDPC codes for iterative decoding algorithms based on belief propagation theoretically. Chilappagari et al. reported the error correction capability of column-weight-three regular LDPC codes under the Gallager A algorithm since the behavior of the iterative decoding algorithm is easily analyzed. In this paper, we investigate the error correction capability of irregular LDPC codes. We also show relations between the decoding failures and 2-cycles of irregular LDPC codes under the Gallager A algorithm.
This paper deals with the irregular binary low-density parity-check (LDPC) codes and two iterative low-complexity decoding algorithms. The first one is the majority error-correcting decoding algorithm, and the second one is iterative erasure-correcting decoding algorithm. The lower bounds on correcting capabilities (the guaranteed corrected error and erasure fraction respectively) of irregular LDPC code under decoding (error and erasure correcting respectively) algorithms with low-complexity were represented. These lower bounds were obtained as a result of analysis of Tanner graph representation of irregular LDPC code. The numerical results, obtained at the end of the paper for proposed lower-bounds achieved similar results for the previously known best lower-bounds for regular LDPC codes and were represented for the first time for the irregular LDPC codes.
This paper deals with the irregular binary low-density parity-check (LDPC) codes with the constituent single parity check (SPC) codes and the error-correcting iterative low-complex decoding algorithm. The lower bound on the error fraction, guaranteed corrected by the considered iterative algorithm, was obtained for the irregular LDPC code for the first time in this paper. This lower bound was obtained as a result of analysis of Tanner graph representation of irregular LDPC code. The number of decoding iterations, required to correct the errors, is a logarithmic function of the code length. The numerical results, obtained at the end of the paper for proposed lower bound achieved similar results for the previously known best lower-bounds for regular LDPC codes and were represented for the first time for the irregular LDPC codes.
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.
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.