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IEEE International Symposium on Information Theory (ISIT)

Improved reversible LDPC codes

David Haley, Alex J. Grant

Published Jan 1, 2005• 9 citations
Publisher

Abstract

We provide a thorough analysis for a class of iteratively encodable low-density parity-check codes. The iterative encoding technique is based upon the graphical representation of the code and allows the decoder circuit to also be used for encoding, thus saving circuit area. The analysis identifies a weakness in the structure of the code, which arises due to a repetitive pattern in its factor graph. We show that the graph supports pseudo-codewords of low pseudo-weight, and relate this to the empirical observation of some near-codewords. We then propose a new recursive technique for constructing iteratively encodable codes which have improved expansion. The new codes offer a large amount of flexibility in the choice of code length and rate, and performance that compares well to both randomly generated and extended Euclidean-geometry codes

DOI
10.1109/isit.2005.1523566

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IEEE Signal Processing Magazine•Jan 1, 2004•61 citations
We provide a thorough analysis for a class of iteratively encodable low-density parity-check codes. The iterative encoding technique is based upon the graphical representation of the code and allows the decoder circuit to also be used for encoding, thus saving circuit area. The analysis identifies a weakness in the structure of the code, which arises due to a repetitive pattern in its factor graph. We show that the graph supports pseudo-codewords of low pseudo-weight, and relate this to the empirical observation of some near-codewords. We then propose a new recursive technique for constructing iteratively encodable codes which have improved expansion. The new codes offer a large amount of flexibility in the choice of code length and rate, and performance that compares well to both randomly generated and extended Euclidean-geometry codes
Low-density parity-check (LDPC) codes may be decoded using a circuit implementation of the sum-product algorithm which maps the factor graph of the code. By reusing the decoder for encoding, both tasks can be performed using the same circuit, thus reducing area and verification requirements. Motivated by this, iterative encoding techniques based upon the graphical representation of the code are proposed. Code design constraints which ensure encoder convergence are presented, and then used to design iteratively encodable codes, while also preventing 4-cycle creation. We show how the Jacobi method for iterative matrix inversion can be applied to finite field matrices, viewed as message passing, and employed as the core of an iterative encoder. We present an algebraic construction of4-cycle free iteratively encodable codes using circulant matrices. Analysis of these codes identifies a weakness in their structure, due to a repetitive pattern in the factor graph. The graph supports pseudo-codewords of low pseudo-weight. In order to remove the repetitive pattern in the graph, we propose a recursive technique for generating iteratively encodable codes. The new codes offer flexibility in the choice of code length and rate, and performance that compares well to randomly generated, quasi-cyclic and extended Euclidean-geometry codes.
This paper presents a geometric approach to the construction of low-density parity-check (LDPC) codes. Four classes of LDPC codes are constructed based on the lines and points of Euclidean and projective geometries over finite fields. Codes of these four classes have good minimum distances and their Tanner (1981) graphs have girth 6. Finite-geometry LDPC codes can be decoded in various ways, ranging from low to high decoding complexity and from reasonably good to very good performance. They perform very well with iterative decoding. Furthermore, they can be put in either cyclic or quasi-cyclic form. Consequently, their encoding can be achieved in linear time and implemented with simple feedback shift registers. This advantage is not shared by other LDPC codes in general and is important in practice. Finite-geometry LDPC codes can be extended and shortened in various ways to obtain other good LDPC codes. Several techniques of extension and shortening are presented. Long extended finite-geometry LDPC codes have been constructed and they achieve a performance only a few tenths of a decibel away from the Shannon theoretical limit with iterative decoding.
The growing popularity of a class of linear block codes called the low-density parity-check (LDPC) codes can be attributed to the low complexity of the iterative decoders, and their potential to achieve performance very close to the Shannon capacity. This makes them an attractive candidate for ECC applications in communication systems. This report proposes methods to systematically construct regular and irregular LDPC codes.A class of regular LDPC codes are constructed from incidence structures in finite geometries like projective geometry and affine geometry. A class of irregular LDPC codes are constructed by systematically splitting blocks of balanced incomplete block designs to achieve desired weight distributions. These codes are decoded iteratively using message-passing algorithms, and the performance of these codes for various channels are presented in this report.The application of iterative decoders is generally limited to a class of codes whose graph representations are free of small cycles. Unfortunately, the large class of conventional algebraic codes, like RS codes, has several four cycles in their graph representations. This report proposes an algorithm that aims to alleviate this drawback by constructing an equivalent graph representation that is free of four cycles. It is theoretically shown that the four-cycle free representation is better suited to iterative erasure decoding than the conventional representation. Also, the new representation is exploited to realize, with limited success, iterative decoding of Reed-Solomon codes over the additive white Gaussian noise channel.Wiberg, Forney, Richardson, Koetter, and Vontobel have made significant contributions in developing theoretical frameworks that facilitate finite length analysis of codes. With an exception of Richardson's, most of the other frameworks are much suited for the analysis of short codes. In this report, we further the understanding of the failures in iterative decoders for the binary symmetric channel. The failures of the decoder are classified into two categories by defining trapping sets and propagating sets. Such a classification leads to a successful estimation of the performance of codes under the Gallager B decoder. Especially, the estimation techniques show great promise in the high signal-to-noise ratio regime where the simulation techniques are less feasible.
Introduction In this chapter we introduce low-density parity-check (LDPC) codes, a class of error correction codes proposed by Gallager in his 1962 PhD thesis 12 years after error correction codes were first introduced by Hamming (published in 1950). Both Hamming codes and LDPC codes are block codes: the messages are broken up into blocks to be encoded at the transmitter and similarly decoded as separate blocks at the receiver. While Hamming codes are short and very structured with a known, fixed, error correction ability, LDPC codes are the opposite, usually long and often constructed pseudo-randomly with only a probabilistic notion of their expected error correction performance. The chapter begins by presenting parity bits as a means to detect and, when more than one is employed, to correct errors in digital data. Block error correction codes are described as a linear combination of parity-check equations and thus defined by their parity-check matrix representation. The graphical representation of codes by Tanner graphs is presented and the necessary graph theoretic concepts introduced. In Section 2.4 iterative decoding algorithms are introduced using a hard decision algorithm (bit flipping), so that the topic is developed first without reference to probability theory. Subsequently the sum–product decoding algorithm is presented. This chapter serves as a self-contained introduction to LDPC codes and their decoding. It is intended that the material presented here will enable the reader to implement LDPC encoders and iterative decoders.
Low density parity check (LDPC) codes with iterative decoding based on belief propagation achieve astonishing error performance close to Shannon limit. No algebraic or geometric method for constructing these codes has been reported and they are largely generated by computer search. As a result, encoding of long LDPC codes is in general very complex. This paper presents two classes of high rate LDPC codes whose constructions are based on finite Euclidean and projective geometries, respectively. These classes of codes a.re cyclic and have good constraint parameters and minimum distances. Cyclic structure adows the use of linear feedback shift registers for encoding. These finite geometry LDPC codes achieve very good error performance with either soft-decision iterative decoding based on belief propagation or Gallager's hard-decision bit flipping algorithm. These codes can be punctured or extended to obtain other good LDPC codes. A generalization of these codes is also presented.
This article describes the different methods to design regular low density parity-check (LDPC) codes with large girth. In graph terms, this corresponds to designing bipartite undirected regular graphs with large girth. Large girth speeds the convergence of iterative decoding and improves the performance at least in the high SNR range, by slowing down the onsetting of the error floor. We reviewed several existing constructions from exhaustive search to highly structured designs based on Euclidean and projective finite geometries and combinatorial designs. We describe GB and TS LDPC codes and compared the BER performance with large girth to the BER performance of random codes. These studies confirm that in the high SNR regime these codes with high girth exhibit better BER performance. The regularity of the codes provides additional advantages that we did not explore in this article like the simplicity of their hardware implementation and fast encoding.