Book contents
- Frontmatter
- Contents
- Preface
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Erasure Channel Models and Bounds
- 4 Low-Density Parity-Check Codes
- 5 Weight Distribution and Minimum Distance of LDPC Codes
- 6 Iterative and Maximum-Likelihood Decoding of LDPC Codes
- 7 Iterative LDPC Decoder Analysis
- 8 Maximum-Likelihood LDPC Decoder Analysis
- 9 Low-Density Parity-Check Codes: Generalizations
- 10 Polar Codes
- 11 Fountain Codes
- Appendix A Applications of Erasure Codes
- Appendix B Random Matrices over Finite Fields
- Index
- References
8 - Maximum-Likelihood LDPC Decoder Analysis
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- Frontmatter
- Contents
- Preface
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Erasure Channel Models and Bounds
- 4 Low-Density Parity-Check Codes
- 5 Weight Distribution and Minimum Distance of LDPC Codes
- 6 Iterative and Maximum-Likelihood Decoding of LDPC Codes
- 7 Iterative LDPC Decoder Analysis
- 8 Maximum-Likelihood LDPC Decoder Analysis
- 9 Low-Density Parity-Check Codes: Generalizations
- 10 Polar Codes
- 11 Fountain Codes
- Appendix A Applications of Erasure Codes
- Appendix B Random Matrices over Finite Fields
- Index
- References
Summary
Maximum-likelihood LDPC decoder analysis. In Chapter 8, the performance of LDPC codes under ML decoding is analyzed. ML decoding is intended here either as the block-wise or the symbol-wise decoding criterion (see Section 2.2). More specifically, the asymptotic analysis on the ML decoding threshold addresses the performance in terms of symbol-wise ML decoding, whereas finite-length bounds are provided for the block error probability under block-wise ML decoding. While the focus is on unstructured LDPC code ensembles, the results in this chapter can be considered to a large extent valid for other LDPC code ensembles.
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- Chapter
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- Coding for Erasure Channels , pp. 202 - 227Publisher: Cambridge University PressPrint publication year: 2026
References
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