Joint source-channel coding of discrete-time signals with continuous amplitudes:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Imperial College Press
©2007
|
| Schriftenreihe: | Communications and signal processing (London, England)
v. 1 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (pages 187-191) and index Cover -- Contents -- Preface -- 1. Introduction -- 2. Joint Source-Channel Coding: An Overview -- 2.1 System Model -- 2.1.1 Channel -- 2.1.2 Encoder -- 2.1.3 Decoder -- 2.2 System Distortion -- 2.3 Optimal Decoder for a Given Encoder -- 2.4 Optimal Encoder -- 2.5 Special Cases -- 2.5.1 Preliminary Remarks -- 2.5.2 Gaussian Source and Gaussian Channel -- 2.5.3 Channels with Binary Input: Channel-Optimized Vector Quantization -- 2.6 Practical Approaches to Source-Channel Coding -- 2.6.1 Systems for Multimedia Transmission -- 2.6.2 Separation of Source and Channel Coding -- 2.6.3 Approaches to Joint Source-Channel Decoding -- 2.6.4 Approaches to Joint Source-Channel Encoding -- 3. Joint Source-Channel Decoding -- 3.1 Introduction and System Model -- 3.2 Near Optimum Joint Source-Channel Decoding -- 3.2.1 Specialization and Generalization -- 3.3 Iterative Source-Channel Decoding (ISCD) -- 3.3.1 Principle and Derivation -- 3.3.2 Efficient Implementation of ISCD by L-values -- - 3.3.3 Simulation Results for ISCD -- 3.4 Quantizer Bit Mappings for ISCD -- 3.4.1 Basic Considerations -- 3.4.2 Optimization by Binary Switching -- 3.4.3 Simulation Results with Optimized Bit Mappings -- 3.5 Conclusions -- 4. Channel-Adaptive Scaled Vector Quantization -- 4.1 Introduction -- 4.2 Memory and Complexity Issues for Vector Quantization (VQ) and Channel-Optimized VQ -- 4.3 Channel-Adaptive Scaled Vector Quantization -- 4.3.1 Basic Principle -- 4.3.2 Optimization of CASVQ -- 4.3.3 Complexity and Memory Requirements of CASVQ for Transmission over Time-Varying Channels -- 4.4 Simulation Results -- 4.5 Conclusions -- 5. Index Assignments for Multiple Descriptions -- 5.1 Introduction -- 5.2 System Model -- 5.3 Optimal Decoder for a Given Index Assignment -- 5.4 Quality Criterion for the Index Assignments -- 5.5 Optimization of the Index Assignments -- 5.5.1 The Complexity Problem -- - 5.5.2 Index Optimization by the Binary Switching Algorithm for a System with a Single Description -- 5.5.3 Binary Switching for Multiple Descriptions -- 5.6 Simulation Results -- 5.7 Conclusions -- 6. Source-Adaptive Modulation -- 6.1 Introduction -- 6.2 Conventional System Model -- 6.2.1 Conventional Hard-Decision Receiver -- 6.2.2 Conventional Soft-Decision Receiver -- 6.3 Principle of Source-Adaptive Modulation (SAM) -- 6.4 SAM for Detection of M-PSK Signal Sets -- 6.4.1 Derivation of the Optimal Solution -- 6.4.2 Test-Point Method -- 6.4.3 Analytical Approximation -- 6.4.4 Simulation Results -- 6.5 SAM for Quadrature Amplitude Modulation -- 6.5.1 Discussion of Potential Signal-Point Locations -- 6.5.2 Simulation Results for SAM with QAM . -- 6.6 Conclusions -- 7. Source-Adaptive Power Allocation -- 7.1 Introduction -- 7.2 System Model -- 7.3 Principle of Source-Adaptive Power Allocation -- 7.4 Conventional Soft-Decision Receiver -- 7.5 Simulation Results -- 7.6 Conclusions -- - 8. Concluding Remarks -- Appendix A Theoretical Performance Limits -- A.1 Preliminary Remarks -- A.2 Important Distortion-Rate Functions -- A.2.1 Memoryless Sources -- A.2.2 Comparison with Practical Quantization Schemes -- A.2.3 Gaussian Sources with Memory -- A.3 Capacities of Practically Important Channels -- A.3.1 Binary Symmetric Channel (BSC) -- A.3.2 Binary Erasure Channel (BEC) -- T$1867 This book provides the first comprehensive and easy-to-read discussion of joint source-channel encoding and decoding for source signals with continuous amplitudes. It is a state-of-the-art presentation of this exciting, thriving field of research, making pioneering contributions to the new concept of source-adaptive modulation. The book starts with the basic theory and the motivation for a joint realization of source and channel coding. Specialized chapters deal with practically relevant scenarios such as iterative source-channel decoding and its optimization for a given encoder, and also improved encoder designs by channel-adaptive quantization or source-adaptive modulation. Although Information Theory is not the main topic of the book - in fact, the concept of joint source-channel coding is contradictory to the classical system design motivated by a questionable practical interpretation of the separation theorem - this theory still provides the ultimate performance limits for any practical system, whether it uses joint source-channel coding or not. Therefore, the theoretical limits are presented in a self-contained appendix, which is a useful reference also for those not directly interested in the main topic of this book |
| Beschreibung: | 1 Online-Ressource (xii, 194 pages) |
| ISBN: | 1281867608 1860948456 1860948464 9781281867605 9781860948459 9781860948466 |
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| 500 | |a Includes bibliographical references (pages 187-191) and index | ||
| 500 | |a Cover -- Contents -- Preface -- 1. Introduction -- 2. Joint Source-Channel Coding: An Overview -- 2.1 System Model -- 2.1.1 Channel -- 2.1.2 Encoder -- 2.1.3 Decoder -- 2.2 System Distortion -- 2.3 Optimal Decoder for a Given Encoder -- 2.4 Optimal Encoder -- 2.5 Special Cases -- 2.5.1 Preliminary Remarks -- 2.5.2 Gaussian Source and Gaussian Channel -- 2.5.3 Channels with Binary Input: Channel-Optimized Vector Quantization -- 2.6 Practical Approaches to Source-Channel Coding -- 2.6.1 Systems for Multimedia Transmission -- 2.6.2 Separation of Source and Channel Coding -- 2.6.3 Approaches to Joint Source-Channel Decoding -- 2.6.4 Approaches to Joint Source-Channel Encoding -- 3. Joint Source-Channel Decoding -- 3.1 Introduction and System Model -- 3.2 Near Optimum Joint Source-Channel Decoding -- 3.2.1 Specialization and Generalization -- 3.3 Iterative Source-Channel Decoding (ISCD) -- 3.3.1 Principle and Derivation -- 3.3.2 Efficient Implementation of ISCD by L-values -- | ||
| 500 | |a - 3.3.3 Simulation Results for ISCD -- 3.4 Quantizer Bit Mappings for ISCD -- 3.4.1 Basic Considerations -- 3.4.2 Optimization by Binary Switching -- 3.4.3 Simulation Results with Optimized Bit Mappings -- 3.5 Conclusions -- 4. Channel-Adaptive Scaled Vector Quantization -- 4.1 Introduction -- 4.2 Memory and Complexity Issues for Vector Quantization (VQ) and Channel-Optimized VQ -- 4.3 Channel-Adaptive Scaled Vector Quantization -- 4.3.1 Basic Principle -- 4.3.2 Optimization of CASVQ -- 4.3.3 Complexity and Memory Requirements of CASVQ for Transmission over Time-Varying Channels -- 4.4 Simulation Results -- 4.5 Conclusions -- 5. Index Assignments for Multiple Descriptions -- 5.1 Introduction -- 5.2 System Model -- 5.3 Optimal Decoder for a Given Index Assignment -- 5.4 Quality Criterion for the Index Assignments -- 5.5 Optimization of the Index Assignments -- 5.5.1 The Complexity Problem -- | ||
| 500 | |a - 5.5.2 Index Optimization by the Binary Switching Algorithm for a System with a Single Description -- 5.5.3 Binary Switching for Multiple Descriptions -- 5.6 Simulation Results -- 5.7 Conclusions -- 6. Source-Adaptive Modulation -- 6.1 Introduction -- 6.2 Conventional System Model -- 6.2.1 Conventional Hard-Decision Receiver -- 6.2.2 Conventional Soft-Decision Receiver -- 6.3 Principle of Source-Adaptive Modulation (SAM) -- 6.4 SAM for Detection of M-PSK Signal Sets -- 6.4.1 Derivation of the Optimal Solution -- 6.4.2 Test-Point Method -- 6.4.3 Analytical Approximation -- 6.4.4 Simulation Results -- 6.5 SAM for Quadrature Amplitude Modulation -- 6.5.1 Discussion of Potential Signal-Point Locations -- 6.5.2 Simulation Results for SAM with QAM . -- 6.6 Conclusions -- 7. Source-Adaptive Power Allocation -- 7.1 Introduction -- 7.2 System Model -- 7.3 Principle of Source-Adaptive Power Allocation -- 7.4 Conventional Soft-Decision Receiver -- 7.5 Simulation Results -- 7.6 Conclusions -- | ||
| 500 | |a - 8. Concluding Remarks -- Appendix A Theoretical Performance Limits -- A.1 Preliminary Remarks -- A.2 Important Distortion-Rate Functions -- A.2.1 Memoryless Sources -- A.2.2 Comparison with Practical Quantization Schemes -- A.2.3 Gaussian Sources with Memory -- A.3 Capacities of Practically Important Channels -- A.3.1 Binary Symmetric Channel (BSC) -- A.3.2 Binary Erasure Channel (BEC) -- T$1867 | ||
| 500 | |a This book provides the first comprehensive and easy-to-read discussion of joint source-channel encoding and decoding for source signals with continuous amplitudes. It is a state-of-the-art presentation of this exciting, thriving field of research, making pioneering contributions to the new concept of source-adaptive modulation. The book starts with the basic theory and the motivation for a joint realization of source and channel coding. Specialized chapters deal with practically relevant scenarios such as iterative source-channel decoding and its optimization for a given encoder, and also improved encoder designs by channel-adaptive quantization or source-adaptive modulation. Although Information Theory is not the main topic of the book - in fact, the concept of joint source-channel coding is contradictory to the classical system design motivated by a questionable practical interpretation of the separation theorem - this theory still provides the ultimate performance limits for any practical system, whether it uses joint source-channel coding or not. Therefore, the theoretical limits are presented in a self-contained appendix, which is a useful reference also for those not directly interested in the main topic of this book | ||
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Datensatz im Suchindex
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| indexdate | 2024-07-10T07:18:34Z |
| institution | BVB |
| isbn | 1281867608 1860948456 1860948464 9781281867605 9781860948459 9781860948466 |
| language | English |
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| spelling | Goertz, Norbert Verfasser aut Joint source-channel coding of discrete-time signals with continuous amplitudes Norbert Goertz London Imperial College Press ©2007 1 Online-Ressource (xii, 194 pages) txt rdacontent c rdamedia cr rdacarrier Communications and signal processing (London, England) v. 1 Includes bibliographical references (pages 187-191) and index Cover -- Contents -- Preface -- 1. Introduction -- 2. Joint Source-Channel Coding: An Overview -- 2.1 System Model -- 2.1.1 Channel -- 2.1.2 Encoder -- 2.1.3 Decoder -- 2.2 System Distortion -- 2.3 Optimal Decoder for a Given Encoder -- 2.4 Optimal Encoder -- 2.5 Special Cases -- 2.5.1 Preliminary Remarks -- 2.5.2 Gaussian Source and Gaussian Channel -- 2.5.3 Channels with Binary Input: Channel-Optimized Vector Quantization -- 2.6 Practical Approaches to Source-Channel Coding -- 2.6.1 Systems for Multimedia Transmission -- 2.6.2 Separation of Source and Channel Coding -- 2.6.3 Approaches to Joint Source-Channel Decoding -- 2.6.4 Approaches to Joint Source-Channel Encoding -- 3. Joint Source-Channel Decoding -- 3.1 Introduction and System Model -- 3.2 Near Optimum Joint Source-Channel Decoding -- 3.2.1 Specialization and Generalization -- 3.3 Iterative Source-Channel Decoding (ISCD) -- 3.3.1 Principle and Derivation -- 3.3.2 Efficient Implementation of ISCD by L-values -- - 3.3.3 Simulation Results for ISCD -- 3.4 Quantizer Bit Mappings for ISCD -- 3.4.1 Basic Considerations -- 3.4.2 Optimization by Binary Switching -- 3.4.3 Simulation Results with Optimized Bit Mappings -- 3.5 Conclusions -- 4. Channel-Adaptive Scaled Vector Quantization -- 4.1 Introduction -- 4.2 Memory and Complexity Issues for Vector Quantization (VQ) and Channel-Optimized VQ -- 4.3 Channel-Adaptive Scaled Vector Quantization -- 4.3.1 Basic Principle -- 4.3.2 Optimization of CASVQ -- 4.3.3 Complexity and Memory Requirements of CASVQ for Transmission over Time-Varying Channels -- 4.4 Simulation Results -- 4.5 Conclusions -- 5. Index Assignments for Multiple Descriptions -- 5.1 Introduction -- 5.2 System Model -- 5.3 Optimal Decoder for a Given Index Assignment -- 5.4 Quality Criterion for the Index Assignments -- 5.5 Optimization of the Index Assignments -- 5.5.1 The Complexity Problem -- - 5.5.2 Index Optimization by the Binary Switching Algorithm for a System with a Single Description -- 5.5.3 Binary Switching for Multiple Descriptions -- 5.6 Simulation Results -- 5.7 Conclusions -- 6. Source-Adaptive Modulation -- 6.1 Introduction -- 6.2 Conventional System Model -- 6.2.1 Conventional Hard-Decision Receiver -- 6.2.2 Conventional Soft-Decision Receiver -- 6.3 Principle of Source-Adaptive Modulation (SAM) -- 6.4 SAM for Detection of M-PSK Signal Sets -- 6.4.1 Derivation of the Optimal Solution -- 6.4.2 Test-Point Method -- 6.4.3 Analytical Approximation -- 6.4.4 Simulation Results -- 6.5 SAM for Quadrature Amplitude Modulation -- 6.5.1 Discussion of Potential Signal-Point Locations -- 6.5.2 Simulation Results for SAM with QAM . -- 6.6 Conclusions -- 7. Source-Adaptive Power Allocation -- 7.1 Introduction -- 7.2 System Model -- 7.3 Principle of Source-Adaptive Power Allocation -- 7.4 Conventional Soft-Decision Receiver -- 7.5 Simulation Results -- 7.6 Conclusions -- - 8. Concluding Remarks -- Appendix A Theoretical Performance Limits -- A.1 Preliminary Remarks -- A.2 Important Distortion-Rate Functions -- A.2.1 Memoryless Sources -- A.2.2 Comparison with Practical Quantization Schemes -- A.2.3 Gaussian Sources with Memory -- A.3 Capacities of Practically Important Channels -- A.3.1 Binary Symmetric Channel (BSC) -- A.3.2 Binary Erasure Channel (BEC) -- T$1867 This book provides the first comprehensive and easy-to-read discussion of joint source-channel encoding and decoding for source signals with continuous amplitudes. It is a state-of-the-art presentation of this exciting, thriving field of research, making pioneering contributions to the new concept of source-adaptive modulation. The book starts with the basic theory and the motivation for a joint realization of source and channel coding. Specialized chapters deal with practically relevant scenarios such as iterative source-channel decoding and its optimization for a given encoder, and also improved encoder designs by channel-adaptive quantization or source-adaptive modulation. Although Information Theory is not the main topic of the book - in fact, the concept of joint source-channel coding is contradictory to the classical system design motivated by a questionable practical interpretation of the separation theorem - this theory still provides the ultimate performance limits for any practical system, whether it uses joint source-channel coding or not. Therefore, the theoretical limits are presented in a self-contained appendix, which is a useful reference also for those not directly interested in the main topic of this book COMPUTERS / Information Theory bisacsh Adaptive signal processing fast Coding theory fast Coding theory Adaptive signal processing Fehlererkennungscode (DE-588)4218726-6 gnd rswk-swf Codierungstheorie (DE-588)4139405-7 gnd rswk-swf Fehlererkennungscode (DE-588)4218726-6 s Codierungstheorie (DE-588)4139405-7 s 1\p DE-604 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=235964 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Goertz, Norbert Joint source-channel coding of discrete-time signals with continuous amplitudes COMPUTERS / Information Theory bisacsh Adaptive signal processing fast Coding theory fast Coding theory Adaptive signal processing Fehlererkennungscode (DE-588)4218726-6 gnd Codierungstheorie (DE-588)4139405-7 gnd |
| subject_GND | (DE-588)4218726-6 (DE-588)4139405-7 |
| title | Joint source-channel coding of discrete-time signals with continuous amplitudes |
| title_auth | Joint source-channel coding of discrete-time signals with continuous amplitudes |
| title_exact_search | Joint source-channel coding of discrete-time signals with continuous amplitudes |
| title_full | Joint source-channel coding of discrete-time signals with continuous amplitudes Norbert Goertz |
| title_fullStr | Joint source-channel coding of discrete-time signals with continuous amplitudes Norbert Goertz |
| title_full_unstemmed | Joint source-channel coding of discrete-time signals with continuous amplitudes Norbert Goertz |
| title_short | Joint source-channel coding of discrete-time signals with continuous amplitudes |
| title_sort | joint source channel coding of discrete time signals with continuous amplitudes |
| topic | COMPUTERS / Information Theory bisacsh Adaptive signal processing fast Coding theory fast Coding theory Adaptive signal processing Fehlererkennungscode (DE-588)4218726-6 gnd Codierungstheorie (DE-588)4139405-7 gnd |
| topic_facet | COMPUTERS / Information Theory Adaptive signal processing Coding theory Fehlererkennungscode Codierungstheorie |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=235964 |
| work_keys_str_mv | AT goertznorbert jointsourcechannelcodingofdiscretetimesignalswithcontinuousamplitudes |