Joint Source-Channel Coding of Discrete-Time Signals with Continuous Amplitudes.
Title:
Joint Source-Channel Coding of Discrete-Time Signals with Continuous Amplitudes.
Author:
Goertz, Norbert.
ISBN:
9781860948466
Personal Author:
Physical Description:
1 online resource (207 pages)
Series:
Communications and Signal Processing ; v.1
Communications and Signal Processing
Contents:
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 -- 2.5.2.1 System Example -- 2.5.2.2 Comparison with Results from Information Theory -- 2.5.3 Channels with Binary Input: Channel-Optimized Vector Quantization -- 2.5.3.1 Binary Symmetric Channel (BSC) -- 2.5.3.2 Channels with Binary Input but with Real Output -- 2.5.3.3 Optimization of the Encoder Mapping: Codebook Training -- 2.5.3.4 Some Simulation Results for COVQ -- 2.6 Practical Approaches to Source-Channel Coding -- 2.6.1 Systems for Multimedia Transmission -- 2.6.2 Separation of Source and Channel Coding -- 2.6.2.1 Channel Codes have a Non-Zero Residual Bit Error Rate -- 2.6.2.2 Source Encoder Output Bits Contain Redundancies -- 2.6.2.3 It Matters, which Bit is in Error -- 2.6.3 Approaches to Joint Source-Channel Decoding -- 2.6.3.1 Unequal Error Protection and Error Concealment -- 2.6.3.2 Source-Controlled Channel Decoding -- 2.6.3.3 Estimation-Based Source Decoding -- 2.6.3.4 Iterative 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.2.1.1 Memoryless Gaussian Source.
A.2.1.2 Memoryless Non-Gaussian Sources -- A.2.2 Comparison with Practical Quantization Schemes -- A.2.2.1 SNR-Values for Optimal Scalar Quantization -- A.2.2.2 Comparison of the DRF with Optimal Scalar and Vector Quantization -- A.2.3 Gaussian Sources with Memory -- A.2.3.1 Simplification for High Rate -- A.2.3.2 Simplification for High Rate and a Linearly Filtered Gaus- sian Source -- A.2.3.3 Quality of the High-Rate Approximation -- A.3 Capacities of Practically Important Channels -- A.3.1 Binary Symmetric Channel (BSC) -- A.3.2 Binary Erasure Channel (BEC) -- A.3.3 Additive White Gaussian Noise (AWGN) Channel -- A.3.3.1 Discrete-Time AWGN Channel -- A.3.3.2 Continuous-Time Band-Limited AWGN Channel -- A.3.3.3 Discrete-Time Two-Dimensional AWGN Channel -- A.3.3.4 Equivalent Power-Normalized Channel Model for Discrete Input Alphabets -- A.3.3.5 Discrete-Time AWGN Channel with Binary Input -- A.3.3.6 Discrete-Time AWGN Channel with Discrete Input -- A.3.3.7 Discrete Time Two-Dimensional AWGN Channel with Discrete Inputs -- A.3.3.8 Some Capacity-Curves for M-PSK -- A.4 Performance Limits for the Transmission of Sources with Continuous-Values over Noisy Channels -- A.4.1 Gaussian Source/AWGN Channel and BSC -- A.4.2 Correlated Gaussian Source/AWGN Channel with Binary Input -- A.4.3 Gaussian Source/Two-Dimensional AWGN Channel with PSK Input -- Appendix B Optimal Decoder for a Given Encoder -- Appendix C Symbol Error Probabilities for M-PSK -- Appendix D Derivative of the Expected Distortion for SAM -- List of Symbols -- List of Acronyms -- Bibliography -- Index.
Abstract:
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. Sample Chapter(s). Chapter 1: Introduction (98 KB). Contents: Joint Source-Channel Coding: An Overview; Joint Source-Channel Decoding; Channel-Adaptive Scaled Vector Quantization; Index Assignments for Multiple Descriptions Vector Quantizers; Source-Adaptive Modulation; Source-Adaptive Power Allocation; Appendices: Theoretical Performance Limits; Optimal Decoder for a Given Encoder; Symbol Error Probabilities for M-PSK; Derivative of the Expected Distortion for SAM. Readership: Students at advanced undergraduate and graduate level; practitioners and academics in Electrical and Communications Engineering, Information Technology
and Computer Science.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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