实例介绍
【实例简介】
经典和现代的信道编码，是研究线性分组码、卷积码、LDPC码以及Turbo码的经典教材
Channel codes Channel coding lies at the heart of digital communication and data storage, and this detailed introduction describes the core theory as well as decoding algorithms implementation details, and performance analyses Professors Ryan and Lin, known for the clarity of their writing, provide the latest information on modern channel codes, including turbo and low-density parity-check(LDPC)codes. They also present detailed coverage of BCH codes Reed-Solomon codes, convolutional codes, finite-geometry codes, and product codes, providing a one-stop resource for both classical and modern coding techniques The opening chapters begin with basic theory to introduce newcomers to the bject, assuming no prior knowledge in the field of channel coding. Subsequent chapters cover the encoding and decoding of the most widely used codes and extend to advanced topics such as code ensemble performance analyses and algebraic code design. Numerous varied and stimulating end-of-chapter problems, 250 in total are also included to test and enhance learning, making this an essential resource for students and practitioners alike William E. Ryan is a Professor in the Department of Electrical and Computer Engi- neering at the University of Arizona, where he has been a faculty member since 1998. Before moving to academia, he held positions in industry for five years. He has published over 100 technical papers and his research interests include coding and signal processing with applications to data storage and data communications Shu Lin is an Adjunct Professor in the department of Electrical and Computer Engineering, University of California, Davis. He has authored and co-authored numerous technical papers and several books, including the successful Error Con- trol Coding(with Daniel J. Costello). He is an IEEE Life Fellow and has received several awards, including the Alexander von Humboldt Research Prize for US Senior Scientists(1996) and the IEEE Third-Millenium Medal(2000 Channel codes Classical and modern WILLIAM E. RYAN University of arizona SHU LIN University of California, Davis 55: L CAMBRIDGE 罗 UNIVERSITY PRESS CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore Sao paulo, Delhi, dubai Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 SRU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Informationonthistitlewww.cambridge.org/9780521848688 o Cambridge University Press 2009 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published in print format 2009 ISBN-13 978-0-511-64182-4 eBook(NetLibrary) ISBN-13978-0-521-84868-8 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain accurate or appropriate Contents Preface page Xlll Coding and Capacity 1.1 Digital Data Communication and Storage 1. 2 Channel-Coding Overview 1.3 Channel-Code Archetype: The(7, 4) Hamming Code 1.4 Design Criteria and Performance Measures 113470 1.5 Channel-Capacity Formulas for Common Channel models 1.5.1 Capacity for Binary-Input Memoryless Channels 1.5.2 Coding Limits for M-ary-Input Memoryless Channels 1.5.3 Coding Limits for Channels with Memory 21 P roblems 24 References 26 Finite Fields, Vector Spaces, Finite Geometries, and Graphs 28 2.1 Sets and binary Operations 28 2.2 Groups 30 2.2.1 Basic Concepts of groups 30 2.2.2 Finite Groups 32 2.2. 3 Subgroups and Cosets 35 2.3 Fields 2.3.1 Definitions and Basic Concepts 38 2.3.2 Finite Fields 41 2.4 Vector Spaces 45 2.4.1 Basic Definitions and Properties 45 2.4.2 Linear Independence and Dimension 46 2.4.3 Finite Vector Spaces over Finite Fields 2.4.4 Inner Products and Dual Spaces 0 2.5 Polynomials over Finite Fields 51 2.6 Construction and Properties of Galois Fields 6 2.6.1 Construction of Galois Fields 56 2.6.2 Some Fundamental Properties of finite Fields 64 2.6.3 Additive and Cyclic Subgroups 69 Contents 2.7 Finite Geometries 2.7.1 Euclidean Geometries 70 2.7.2 Projective geometries 8 Graph 80 2.8.1 Basic Concepts 80 2.8.2 Paths and cycles 84 2.8.3 Bipartite Graphs 86 Problems References A dix a Linear Block code 94 3.1 Introduction to Linear Block Codes 94 3.1.1 Generator and Parity-Check Matrices 3.1.2 Error Detection with Linear block codes 3.1.3 Weight Distribution and minimum hamming distance of a Linear block code 99 3.1.4 Decoding of Linear Block Codes 102 3.2 Cyclic Codes 106 3.3 BCh Codes 111 3.3.1 Code Construction 3.3.2 Decoding 114 3.4 Nonbinary Linear Block Codes and Reed-Solomon Codes 121 3.5 Product. Interleaved and Concatenated Codes 129 3.5.1 Product Codes 129 3.5.2 Interleaved Codes 130 3.5.3 Concatenated Codes 131 3.6 Quasi-Cyclic Codes 133 3.7 Repetition and Single-Parity-Check Codes 142 Problems 143 References 145 Convolutional Codes 147 4.1 The Convolutional Code Archetype 147 4.2 Algebraic Description of Convolutional Codes 149 4.3 Encoder Realizations and classifications 152 4.3.1 Choice of Encoder clas 157 4.3.2 Catastrophic Encoders 158 4.3.3 Minimal encoders 159 4.3.4 Design of Convolutional Codes 163 4.4 Alternative Convolutional Code Representations 163 4.4.1 Convolutional codes as semi-Infinite Linear codes 164 4.4.2 Graphical Representations for Convolutional Code encoders 170 Contents 4.5 Trellis-Based Decoders 171 4.5.1 MLSD and the Viterbi algorithm 172 4.5. 2 Differential Viterbi Decoding 177 4.5.3 Bit-wise MaP Decoding and the bcjr algorithm 180 4.6 Performance estimates for Trellis-Based decoders 187 4.6.1 ML Decoder Performance for block Codes 187 4.6.2 Weight Enumerators for Convolutional Codes 189 4.6.3 ML Decoder Performance for Convolutional Codes P oriens 195 References Low-Density Parity-Check Codes 201 5.1 Representations of LDPC Codes 201 5.1.1 Matrix Representation 201 5.1.2 Graphical representation 202 5.2 Classifications of LDPc code 205 5.2.1 Generalized LdPc codes 207 5.3 Message Passing and the Turbo Principle 5. 4 The Sum-Product Algorithm 213 5.4.1 Overview 5.4.2 Repetition Code MAP Decoder and APP Processor 216 5.4.3 Single-Parity-Check Code MAP Decoder and APP Processor 217 5.4.4 The Gallager sPA Decoder 218 5.4.5 The Box-Plus sPa decoder 222 5.4.6 Comments on the Performance of the spa decoder 225 5.5 Reduced-Complexity SPA Approximations 226 5.5.1 The Min-Sum Decoder 226 5.5.2 The Attenuated and Offset Min-Sum Decoders 229 5.5.3 The Min-Sum-with-Correction Decoder 231 5.5.4 The Approximate Min* Decoder 5.5.5 The Richardson/Novichkov Decoder 234 5.5.6 The Reduced-Complexity Box-Plus Decoder 236 5.6 Iterative Decoders for Generalized lDPC codes 241 5.7 Decoding Algorithms for the BEC and the bsc 243 5.7.1 Iterative Erasure Filling for the bec 243 5.7.2 ML Decoder for the bec 244 5.7.3 Gallager's Algorithm A and Algorithm B for the BSC 246 5.7.4 The Bit-Flipping Algorithm for the BSC 247 5.8 Concluding Remarks 248 Problems 248 References 254 Contents Computer-Based Design of LDPC Codes 257 6.1 The Original LDPC Codes 257 6.1.1 Gallager Codes 257 6.1.2 MacKay Codes 258 6.2 The PEG and ace Code-Design algorithms 259 6.2. 1 The PEG Algorithm 259 6.2.2 The ACE Algorithm 260 6.3 Protograph LDPC Codes 261 6.3.1 Decoding Architectures for Protograph Codes 264 6.4 Multi-Edge-Type LDPC Codes 265 6.5 Single-Accumulator -Based LDPC Codes 266 6.5.1 Repeat-Accumulate Codes 267 6.5.2 Irregular Repeat-Accumulate Codes 267 6.5.3 Generalized Accumulator LDPC codes 6.6 Double-Accumulator-Based LdPC codes 277 6.6. 1 Irregular Repeat-Accumulate- Accumulate Codes 6.6.2 Accumulate-Repeat-Accumulate Codes 279 6.7 Accumulator-Based Codes in Standards 285 6.8 Generalized LDPC codes 287 6.8.1 A Rate-1/2 G-LDPC Code Problems References Turbo Codes 298 7. 1 Parallel-Concatenated Convolutional Codes 298 7.1.1 Critical Properties of RSC Codes 9 7.1.2 Critical Properties of the Interleaver 300 7.1.3 The Puncturer 7. 1.4 Performance Estimate on the bl-awgnc 301 7.2 The Pccc iterative Decoder 306 7. 2.1 Overview of the iterative Decoder 308 7.2.2 Decoder Details 309 7.2.3 Summary of the PCCC Iterative Decoder 313 7. 2.4 Lower-Complexity Approximations 316 7.3 Serial-Concatenated Convolutional Codes 320 7.3.1 Performance Estimate on the bi-aWgnc 320 7.3.2 The SCCC Iterative Decode 323 7.3.3 Summary of the SCCc Iterative Decoder 7. 4 Turbo Product Codes 7.4.1 Turbo Decoding of Product Codes 330 Problems 335 References 337 【实例截图】
【核心代码】
经典和现代的信道编码，是研究线性分组码、卷积码、LDPC码以及Turbo码的经典教材
Channel codes Channel coding lies at the heart of digital communication and data storage, and this detailed introduction describes the core theory as well as decoding algorithms implementation details, and performance analyses Professors Ryan and Lin, known for the clarity of their writing, provide the latest information on modern channel codes, including turbo and low-density parity-check(LDPC)codes. They also present detailed coverage of BCH codes Reed-Solomon codes, convolutional codes, finite-geometry codes, and product codes, providing a one-stop resource for both classical and modern coding techniques The opening chapters begin with basic theory to introduce newcomers to the bject, assuming no prior knowledge in the field of channel coding. Subsequent chapters cover the encoding and decoding of the most widely used codes and extend to advanced topics such as code ensemble performance analyses and algebraic code design. Numerous varied and stimulating end-of-chapter problems, 250 in total are also included to test and enhance learning, making this an essential resource for students and practitioners alike William E. Ryan is a Professor in the Department of Electrical and Computer Engi- neering at the University of Arizona, where he has been a faculty member since 1998. Before moving to academia, he held positions in industry for five years. He has published over 100 technical papers and his research interests include coding and signal processing with applications to data storage and data communications Shu Lin is an Adjunct Professor in the department of Electrical and Computer Engineering, University of California, Davis. He has authored and co-authored numerous technical papers and several books, including the successful Error Con- trol Coding(with Daniel J. Costello). He is an IEEE Life Fellow and has received several awards, including the Alexander von Humboldt Research Prize for US Senior Scientists(1996) and the IEEE Third-Millenium Medal(2000 Channel codes Classical and modern WILLIAM E. RYAN University of arizona SHU LIN University of California, Davis 55: L CAMBRIDGE 罗 UNIVERSITY PRESS CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore Sao paulo, Delhi, dubai Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 SRU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Informationonthistitlewww.cambridge.org/9780521848688 o Cambridge University Press 2009 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published in print format 2009 ISBN-13 978-0-511-64182-4 eBook(NetLibrary) ISBN-13978-0-521-84868-8 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain accurate or appropriate Contents Preface page Xlll Coding and Capacity 1.1 Digital Data Communication and Storage 1. 2 Channel-Coding Overview 1.3 Channel-Code Archetype: The(7, 4) Hamming Code 1.4 Design Criteria and Performance Measures 113470 1.5 Channel-Capacity Formulas for Common Channel models 1.5.1 Capacity for Binary-Input Memoryless Channels 1.5.2 Coding Limits for M-ary-Input Memoryless Channels 1.5.3 Coding Limits for Channels with Memory 21 P roblems 24 References 26 Finite Fields, Vector Spaces, Finite Geometries, and Graphs 28 2.1 Sets and binary Operations 28 2.2 Groups 30 2.2.1 Basic Concepts of groups 30 2.2.2 Finite Groups 32 2.2. 3 Subgroups and Cosets 35 2.3 Fields 2.3.1 Definitions and Basic Concepts 38 2.3.2 Finite Fields 41 2.4 Vector Spaces 45 2.4.1 Basic Definitions and Properties 45 2.4.2 Linear Independence and Dimension 46 2.4.3 Finite Vector Spaces over Finite Fields 2.4.4 Inner Products and Dual Spaces 0 2.5 Polynomials over Finite Fields 51 2.6 Construction and Properties of Galois Fields 6 2.6.1 Construction of Galois Fields 56 2.6.2 Some Fundamental Properties of finite Fields 64 2.6.3 Additive and Cyclic Subgroups 69 Contents 2.7 Finite Geometries 2.7.1 Euclidean Geometries 70 2.7.2 Projective geometries 8 Graph 80 2.8.1 Basic Concepts 80 2.8.2 Paths and cycles 84 2.8.3 Bipartite Graphs 86 Problems References A dix a Linear Block code 94 3.1 Introduction to Linear Block Codes 94 3.1.1 Generator and Parity-Check Matrices 3.1.2 Error Detection with Linear block codes 3.1.3 Weight Distribution and minimum hamming distance of a Linear block code 99 3.1.4 Decoding of Linear Block Codes 102 3.2 Cyclic Codes 106 3.3 BCh Codes 111 3.3.1 Code Construction 3.3.2 Decoding 114 3.4 Nonbinary Linear Block Codes and Reed-Solomon Codes 121 3.5 Product. Interleaved and Concatenated Codes 129 3.5.1 Product Codes 129 3.5.2 Interleaved Codes 130 3.5.3 Concatenated Codes 131 3.6 Quasi-Cyclic Codes 133 3.7 Repetition and Single-Parity-Check Codes 142 Problems 143 References 145 Convolutional Codes 147 4.1 The Convolutional Code Archetype 147 4.2 Algebraic Description of Convolutional Codes 149 4.3 Encoder Realizations and classifications 152 4.3.1 Choice of Encoder clas 157 4.3.2 Catastrophic Encoders 158 4.3.3 Minimal encoders 159 4.3.4 Design of Convolutional Codes 163 4.4 Alternative Convolutional Code Representations 163 4.4.1 Convolutional codes as semi-Infinite Linear codes 164 4.4.2 Graphical Representations for Convolutional Code encoders 170 Contents 4.5 Trellis-Based Decoders 171 4.5.1 MLSD and the Viterbi algorithm 172 4.5. 2 Differential Viterbi Decoding 177 4.5.3 Bit-wise MaP Decoding and the bcjr algorithm 180 4.6 Performance estimates for Trellis-Based decoders 187 4.6.1 ML Decoder Performance for block Codes 187 4.6.2 Weight Enumerators for Convolutional Codes 189 4.6.3 ML Decoder Performance for Convolutional Codes P oriens 195 References Low-Density Parity-Check Codes 201 5.1 Representations of LDPC Codes 201 5.1.1 Matrix Representation 201 5.1.2 Graphical representation 202 5.2 Classifications of LDPc code 205 5.2.1 Generalized LdPc codes 207 5.3 Message Passing and the Turbo Principle 5. 4 The Sum-Product Algorithm 213 5.4.1 Overview 5.4.2 Repetition Code MAP Decoder and APP Processor 216 5.4.3 Single-Parity-Check Code MAP Decoder and APP Processor 217 5.4.4 The Gallager sPA Decoder 218 5.4.5 The Box-Plus sPa decoder 222 5.4.6 Comments on the Performance of the spa decoder 225 5.5 Reduced-Complexity SPA Approximations 226 5.5.1 The Min-Sum Decoder 226 5.5.2 The Attenuated and Offset Min-Sum Decoders 229 5.5.3 The Min-Sum-with-Correction Decoder 231 5.5.4 The Approximate Min* Decoder 5.5.5 The Richardson/Novichkov Decoder 234 5.5.6 The Reduced-Complexity Box-Plus Decoder 236 5.6 Iterative Decoders for Generalized lDPC codes 241 5.7 Decoding Algorithms for the BEC and the bsc 243 5.7.1 Iterative Erasure Filling for the bec 243 5.7.2 ML Decoder for the bec 244 5.7.3 Gallager's Algorithm A and Algorithm B for the BSC 246 5.7.4 The Bit-Flipping Algorithm for the BSC 247 5.8 Concluding Remarks 248 Problems 248 References 254 Contents Computer-Based Design of LDPC Codes 257 6.1 The Original LDPC Codes 257 6.1.1 Gallager Codes 257 6.1.2 MacKay Codes 258 6.2 The PEG and ace Code-Design algorithms 259 6.2. 1 The PEG Algorithm 259 6.2.2 The ACE Algorithm 260 6.3 Protograph LDPC Codes 261 6.3.1 Decoding Architectures for Protograph Codes 264 6.4 Multi-Edge-Type LDPC Codes 265 6.5 Single-Accumulator -Based LDPC Codes 266 6.5.1 Repeat-Accumulate Codes 267 6.5.2 Irregular Repeat-Accumulate Codes 267 6.5.3 Generalized Accumulator LDPC codes 6.6 Double-Accumulator-Based LdPC codes 277 6.6. 1 Irregular Repeat-Accumulate- Accumulate Codes 6.6.2 Accumulate-Repeat-Accumulate Codes 279 6.7 Accumulator-Based Codes in Standards 285 6.8 Generalized LDPC codes 287 6.8.1 A Rate-1/2 G-LDPC Code Problems References Turbo Codes 298 7. 1 Parallel-Concatenated Convolutional Codes 298 7.1.1 Critical Properties of RSC Codes 9 7.1.2 Critical Properties of the Interleaver 300 7.1.3 The Puncturer 7. 1.4 Performance Estimate on the bl-awgnc 301 7.2 The Pccc iterative Decoder 306 7. 2.1 Overview of the iterative Decoder 308 7.2.2 Decoder Details 309 7.2.3 Summary of the PCCC Iterative Decoder 313 7. 2.4 Lower-Complexity Approximations 316 7.3 Serial-Concatenated Convolutional Codes 320 7.3.1 Performance Estimate on the bi-aWgnc 320 7.3.2 The SCCC Iterative Decode 323 7.3.3 Summary of the SCCc Iterative Decoder 7. 4 Turbo Product Codes 7.4.1 Turbo Decoding of Product Codes 330 Problems 335 References 337 【实例截图】
【核心代码】
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