Book contents
- Frontmatter
- Contents
- Preface
- 1 Coding and Capacity
- 2 Finite Fields, Vector Spaces, Finite Geometries, and Graphs
- 3 Linear Block Codes
- 4 Convolutional Codes
- 5 Low-Density Parity-Check Codes
- 6 Computer-Based Design of LDPC Codes
- 7 Turbo Codes
- 8 Ensemble Enumerators for Turbo and LDPC Codes
- 9 Ensemble Decoding Thresholds for LDPC and Turbo Codes
- 10 Finite-Geometry LDPC Codes
- 11 Constructions of LDPC Codes Based on Finite Fields
- 12 LDPC Codes Based on Combinatorial Designs, Graphs, and Superposition
- 13 LDPC Codes for Binary Erasure Channels
- 14 Nonbinary LDPC Codes
- 15 LDPC Code Applications and Advanced Topics
- Index
5 - Low-Density Parity-Check Codes
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- 1 Coding and Capacity
- 2 Finite Fields, Vector Spaces, Finite Geometries, and Graphs
- 3 Linear Block Codes
- 4 Convolutional Codes
- 5 Low-Density Parity-Check Codes
- 6 Computer-Based Design of LDPC Codes
- 7 Turbo Codes
- 8 Ensemble Enumerators for Turbo and LDPC Codes
- 9 Ensemble Decoding Thresholds for LDPC and Turbo Codes
- 10 Finite-Geometry LDPC Codes
- 11 Constructions of LDPC Codes Based on Finite Fields
- 12 LDPC Codes Based on Combinatorial Designs, Graphs, and Superposition
- 13 LDPC Codes for Binary Erasure Channels
- 14 Nonbinary LDPC Codes
- 15 LDPC Code Applications and Advanced Topics
- Index
Summary
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.
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- Channel CodesClassical and Modern, pp. 201 - 256Publisher: Cambridge University PressPrint publication year: 2009
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