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低密度パリティ検査符号の統計力学的解析(修士論文(1998年度))

Tatsuto Murayama

Published Sep 20, 1999• 0 citations
OpenAlex

Abstract

The performance of low-density parity-check codes is investigated via methods of statistical mechanics. Low-density parity-check codes is first invented by Gallager, which was abandoned shortly after its introduction due to the limited computational abilities and recently rediscovered by MacKay and Neal as MN codes. In these codes, a message is encoded to the codeword which comprises products of the message bits selected by two randomly-constructed sparse matrices. The typical case analysis of statistical mechanics indicates a practical property of the particular family of the codes, which could not be found within the framework of worst case analysis. Further, decoding aspects are considered by investigating solutions obtained by a mean field approach, which is identical to the commonly used belief propagation.

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•Jan 1, 2005•0 citations
The performance of low-density parity-check codes is investigated via methods of statistical mechanics. Low-density parity-check codes is first invented by Gallager, which was abandoned shortly after its introduction due to the limited computational abilities and recently rediscovered by MacKay and Neal as MN codes. In these codes, a message is encoded to the codeword which comprises products of the message bits selected by two randomly-constructed sparse matrices. The typical case analysis of statistical mechanics indicates a practical property of the particular family of the codes, which could not be found within the framework of worst case analysis. Further, decoding aspects are considered by investigating solutions obtained by a mean field approach, which is identical to the commonly used belief propagation.
A variation of Gallager error-correcting codes is investigated using statistical mechanics. In codes of this type, a given message is encoded into a codeword that comprises Boolean sums of message bits selected by two randomly constructed sparse matrices. The similarity of these codes to Ising spin systems with random interaction makes it possible to assess their typical performance by analytical methods developed in the study of disordered systems. The typical case solutions obtained via the replica method are consistent with those obtained in simulations using belief propagation decoding. We discuss the practical implications of the results obtained and suggest a computationally efficient construction for one of the more practical configurations.
We review recent theoretical progress on the statistical mechanics of error correcting codes, focusing on low-density parity-check (LDPC) codes in general, and on Gallager and MacKay–Neal codes in particular. By exploiting the relation between LDPC codes and Ising spin systems with multi-spin interactions, one can carry out a statistical mechanics based analysis that determines the practical and theoretical limitations of various code constructions, corresponding to dynamical and thermodynamical transitions, respectively, as well as the behaviour of error-exponents averaged over the corresponding code ensemble as a function of channel noise. We also contrast the results obtained using methods of statistical mechanics with those derived in the information theory literature, and show how these methods can be generalized to include other channel types and related communication problems.
Low-density parity-check (LDPC) codes are a class of linear block codes with implementable decoders, which provide near-capacity performance on a large set of data-transmission and data-storage channels. LDPC codes were invented by Gallager in his 1960 doctoral dissertation and were mostly ignored during the 35 years that followed. One notable exception is the important work of Tanner in 1981, in which Tanner generalized LDPC codes and introduced a graphical representation of LDPC codes, now called a Tanner graph. The study of LDPC codes was resurrected in the mid 1990s with the work of MacKay, Luby, and others [3–6], who noticed, apparently independently of Gallager's work, the advantages of linear block codes with sparse (low-density) parity-check matrices. This chapter introduces LDPC codes and creates a foundation for further study of LDPC codes in later chapters. We start with the fundamental representations of LDPC codes via parity-check matrices and Tanner graphs. We then learn about the decoding advantages of linear codes that possess sparse parity-check matrices. We will see that this sparseness characteristic makes the code amenable to various iterative decoding algorithms, which in many instances provide near optimal performance. Gallager of course recognized the decoding advantages of such low-density parity-check codes and he proposed a decoding algorithm for the BI-AWGNC and a few others for the BSC. These algorithms have received much scrutiny in the past decade, and are still being studied.
LDPC codes were first introduced by Robert Gallager in 1960. Due to the complexity of the codes and the limitations of the then rudimentary computer resources the codes were neglected as a viable form of FEC. LDPC codes were rediscovered by Tanner in 1981 when he generalized the codes and provided a means of graphical representation of LDPC codes. LDPC codes were again neglected until the work of MacKay et al in the mid to late 1990’s resurrected interest in the codes when they were discovered to out perform the then premium Turbo codes. This dissertation specifically describes the process of encoding and decoding LDPC codes and demonstrates the performance comparison between the various types of decoders in terms of bit error rate performance factors.
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.
The class of low-density parity-check (LDPC) codes was introduced by Gallager in his thesis in 1961 [2]. Until the invention of turbo codes, they were largely neglected, but received a lot of interest lately, due to the possibility of designing LDPC codes within 0:0045 dB of the Shannon limit. We review in this project techniques used to analyze the performance of LDPC codes under a message-passing decoding algorithm, namely belief propagation (BP) [4][5]. This analysis gives us the tools to design good LDPC codes. We will flrst take the simpler example of transmission over the class of binary erasure channels (BEC). We will then see how it is possible to extend the results for the BEC to the much more general class of symmetric channels.