{"product_id":"joint-source-channel-coding-isbn-9781119978527","title":"Joint Source-Channel Coding","description":"\u003cb\u003eJoint Source-Channel Coding\u003c\/b\u003e \u003cp\u003e\u003cb\u003eConsolidating knowledge on Joint Source-Channel Coding (JSCC), this book provides an indispensable resource on a key area of performance enhancement for communications networks\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePresenting in one volume the key theories, concepts and important developments in the area of Joint Source-Channel Coding (JSCC), this book provides the fundamental material needed to enhance the performance of digital and wireless communication systems and networks. \u003c\/p\u003e\u003cp\u003eIt comprehensively introduces JSCC technologies for communications systems, including coding and decoding algorithms, and emerging applications of JSCC in current wireless communications. The book covers the full range of theoretical and technical areas before concluding with a section considering recent applications and emerging designs for JSCC. A methodical reference for academic and industrial researchers, development engineers, system engineers, system architects and software engineers, this book: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Explains how JSCC leads to high performance in communication systems and networks\u003c\/li\u003e \u003cli\u003eConsolidates key material from multiple disparate sources\u003c\/li\u003e \u003cli\u003e Is an ideal reference for graduate-level courses on digital or wireless communications, as well as courses on information theory\u003c\/li\u003e \u003cli\u003e Targets professionals involved with digital and wireless communications and networking systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction and Background 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Simplified Model for a Communication System 2\u003c\/p\u003e \u003cp\u003e1.2 Entropy and Information 3\u003c\/p\u003e \u003cp\u003e1.3 Introduction to Source Coding 6\u003c\/p\u003e \u003cp\u003e1.3.1 Sampling and Quantization of Signals 6\u003c\/p\u003e \u003cp\u003e1.3.2 Source Coding of Quantized Signals 9\u003c\/p\u003e \u003cp\u003e1.3.3 Distortion and Rate-distortion Theory 13\u003c\/p\u003e \u003cp\u003e1.4 Channels, Channel Coding, and Capacity 17\u003c\/p\u003e \u003cp\u003e1.4.1 Channel Models 17\u003c\/p\u003e \u003cp\u003e1.4.2 Wireless Channels 19\u003c\/p\u003e \u003cp\u003e1.4.3 Channel Coding and Channel Capacity 23\u003c\/p\u003e \u003cp\u003e1.5 Layered Model for a Communication System 26\u003c\/p\u003e \u003cp\u003e1.6 Distortion, Quality of Service, and Quality of Experience 30\u003c\/p\u003e \u003cp\u003e1.6.1 Objective Measurements of Distortion or Quality 31\u003c\/p\u003e \u003cp\u003e1.6.2 Subjective and Perceptually Based Measurements of Distortion or Quality 32\u003c\/p\u003e \u003cp\u003e1.7 Shannon’s Separation Principle and Joint Source–Channel Coding 36\u003c\/p\u003e \u003cp\u003e1.8 Major Classes of Joint Source–Channel Coding Techniques 40\u003c\/p\u003e \u003cp\u003eReferences 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Source Coding and Signal Compression 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Types of Sources 43\u003c\/p\u003e \u003cp\u003e2.2 Lossless Compression 46\u003c\/p\u003e \u003cp\u003e2.2.1 Entropy Coding 47\u003c\/p\u003e \u003cp\u003e2.2.2 Predictive Coding 52\u003c\/p\u003e \u003cp\u003e2.3 Lossy Compression 54\u003c\/p\u003e \u003cp\u003e2.3.1 Quantization 54\u003c\/p\u003e \u003cp\u003e2.3.2 Differential Coding 62\u003c\/p\u003e \u003cp\u003e2.3.3 Transform Coding 63\u003c\/p\u003e \u003cp\u003e2.3.4 Subband and Wavelet Coding 65\u003c\/p\u003e \u003cp\u003e2.4 Embedded and Layered Coding 68\u003c\/p\u003e \u003cp\u003e2.5 Coding of Practical Sources 71\u003c\/p\u003e \u003cp\u003e2.5.1 Image Coding - JPEG 71\u003c\/p\u003e \u003cp\u003e2.5.2 Embedded Image Coding – SPIHT 75\u003c\/p\u003e \u003cp\u003e2.5.3 Video Coding 78\u003c\/p\u003e \u003cp\u003e2.5.4 Speech Coding 83\u003c\/p\u003e \u003cp\u003eReferences 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Channel Coding 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Linear Block Codes 87\u003c\/p\u003e \u003cp\u003e3.1.1 Binary Linear Block Codes 90\u003c\/p\u003e \u003cp\u003e3.1.2 Generator Matrix, Parity-Check Matrix, and Syndrome Testing 91\u003c\/p\u003e \u003cp\u003e3.1.3 Common Linear Block Codes 92\u003c\/p\u003e \u003cp\u003e3.1.4 Error and Erasure Correction with Block Codes 95\u003c\/p\u003e \u003cp\u003e3.2 Convolutional Codes 97\u003c\/p\u003e \u003cp\u003e3.2.1 Code Characterization: State and Trellis Diagrams 98\u003c\/p\u003e \u003cp\u003e3.2.2 Maximum Likelihood (ML) Decoding 100\u003c\/p\u003e \u003cp\u003e3.2.3 The Viterbi Algorithm 101\u003c\/p\u003e \u003cp\u003e3.2.4 Error Correction Performance 104\u003c\/p\u003e \u003cp\u003e3.3 Modified Linear Codes (Puncturing, Shortening, Expurgating, Extending, Augmenting, and Lengthening) 105\u003c\/p\u003e \u003cp\u003e3.4 Rate-Compatible Channel Codes 105\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Concatenated Joint Source–Channel Coding 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Concatenated JSCC Bit Rate Allocation 111\u003c\/p\u003e \u003cp\u003e4.2 Performance Characterization 119\u003c\/p\u003e \u003cp\u003e4.2.1 Practical Source and Channel Codecs 119\u003c\/p\u003e \u003cp\u003e4.3 Application Cases 131\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Unequal Error Protection Source–Channel Coding 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Effect of Channel Errors on Source Encoded Data 135\u003c\/p\u003e \u003cp\u003e5.2 Priority Encoding Transmission Schemes for Unequal Loss Protection 142\u003c\/p\u003e \u003cp\u003e5.3 Dynamic Programming Algorithm for Optimal UEP 147\u003c\/p\u003e \u003cp\u003e5.4 Unequal Error Protection Using Digital Fountain Codes 163\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Source–Channel Coding with Feedback 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Joint Source–Channel Coding Formulation for a System with ACK\/NACK Feedback 173\u003c\/p\u003e \u003cp\u003e6.1.1 Performance Measurement 175\u003c\/p\u003e \u003cp\u003e6.1.2 Classification of the Transmitters 176\u003c\/p\u003e \u003cp\u003e6.1.3 Decoder Structure and Design 177\u003c\/p\u003e \u003cp\u003e6.2 Packet Combining for Joint Source–Channel ARQ over Memoryless Channels 179\u003c\/p\u003e \u003cp\u003e6.2.1 Decoder Design Problem 179\u003c\/p\u003e \u003cp\u003e6.3 Pruned Tree-Structured Quantization in Noise and Feedback 193\u003c\/p\u003e \u003cp\u003e6.3.1 Pruned Tree-Structured Vector Quantizers 194\u003c\/p\u003e \u003cp\u003e6.3.2 Progressive Transmission with ACK\/NACK Feedback of TSVQ-Encoded Sources 195\u003c\/p\u003e \u003cp\u003e6.3.3 Progressive Transmission and Receiver-Driven Rate Control 204\u003c\/p\u003e \u003cp\u003e6.4 Delay-Constrained JSCC Using Incremental Redundancy with Feedback 205\u003c\/p\u003e \u003cp\u003e6.4.1 System Description 205\u003c\/p\u003e \u003cp\u003e6.4.2 Optimal Source and Channel Rate Allocations Design 208\u003c\/p\u003e \u003cp\u003e6.4.3 Performance 213\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Quantizers Designed for Noisy Channels 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Channel-Optimized Quantizers 223\u003c\/p\u003e \u003cp\u003e7.2 Scalar Quantizer Design 227\u003c\/p\u003e \u003cp\u003e7.3 Vector Quantizer Design 234\u003c\/p\u003e \u003cp\u003e7.4 Channel Mismatch Considerations 245\u003c\/p\u003e \u003cp\u003e7.5 Structured Vector Quantizers 249\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Error-Resilient Source Coding 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Multiple-Description Coding 257\u003c\/p\u003e \u003cp\u003e8.2 Error-Resilient Coded Bit Streams 273\u003c\/p\u003e \u003cp\u003e8.2.1 Robust Entropy Coding 273\u003c\/p\u003e \u003cp\u003e8.2.2 Predictive Coding Mode Selection 279\u003c\/p\u003e \u003cp\u003eReferences 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Analog and Hybrid Digital–Analog JSCC Techniques 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Analog Joint Source–Channel Coding Techniques 283\u003c\/p\u003e \u003cp\u003e9.1.1 Analog Joint Source–Channel Coding in Vector Spaces 283\u003c\/p\u003e \u003cp\u003e9.1.2 Analog Joint Source–Channel Coding Through Artificial Neural Networks 293\u003c\/p\u003e \u003cp\u003e9.2 Hybrid Digital–Analog JSCC Techniques 297\u003c\/p\u003e \u003cp\u003eReferences 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Joint Source–Channel Decoding 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Source-Controlled Channel Decoding 305\u003c\/p\u003e \u003cp\u003e10.2 Exploiting Residual Redundancy at the Decoder 314\u003c\/p\u003e \u003cp\u003e10.2.1 The Soft Output Viterbi Algorithm (SOVA) 315\u003c\/p\u003e \u003cp\u003e10.2.2 Exploiting Residual Redundancy to Estimate A Priori Information 318\u003c\/p\u003e \u003cp\u003e10.3 Iterative Source–Channel Decoding 323\u003c\/p\u003e \u003cp\u003e10.3.1 The Channel Coding Optimal Estimation Algorithm 328\u003c\/p\u003e \u003cp\u003e10.3.2 Channel Coding Optimal Estimation Applied to JSCD 330\u003c\/p\u003e \u003cp\u003eReferences 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Recent Applications and Emerging Designs in Source–Channel Coding 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Source–Channel Coding for Wireless Sensor Networks 335\u003c\/p\u003e \u003cp\u003e11.2 Extending Network Capacity Through JSCC 343\u003c\/p\u003e \u003cp\u003e11.2.1 Video Telephony Calls as Application Example 345\u003c\/p\u003e \u003cp\u003e11.2.2 CDMA Statistical Multiplexing Resource Allocation and Flow Control 347\u003c\/p\u003e \u003cp\u003e11.2.3 Overhead from Communicating Rate-Distortion Data 354\u003c\/p\u003e \u003cp\u003e11.2.4 Analysis for Dynamic Call Traffic and Admission Control 356\u003c\/p\u003e \u003cp\u003e11.2.5 Performance Results 358\u003c\/p\u003e \u003cp\u003e11.3 Source–Channel Coding and Cognitive Radios 364\u003c\/p\u003e \u003cp\u003e11.4 Design of JSCC Schemes Based on Artificial Neural Networks 374\u003c\/p\u003e \u003cp\u003eReferences 378\u003c\/p\u003e \u003cp\u003eIndex 381\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAndres Kwasinski, Rochester Institute of Technology, USA\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Kwasinski\u003c\/b\u003e received his Ph.D. degree in Electrical and Computer Engineering from the University of Maryland in 2004. He is currently a Professor with the Department of Computer Engineering, Rochester Institute of Technology, Rochester, New York. Prior to this he was with Texas Instruments Inc., the Department of Electrical and Computer Engineering at the University of Maryland, and Lucent Technologies. Dr. Kwasinski has been a member of the IEEE Signal Processing Magazine Editorial Board, as Associate Editor and Area Editor for over twelve years. He was Editor for the IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters, the Globecom 2010 Workshop Co-Chair and the Chair of the IEEE Multimedia Technical Committee Interest Group on Distributed and Sensor Networks for Mobile Media Computing and Applications. He is a Senior Member of the IEEE. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVinay Chande, Qualcomm Inc., USA\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVinay Chande\u003c\/b\u003e has a Ph.D. in Electrical Engineering from the University of Maryland and his engineering education from Indian Institute of Technology, Mumbai. Dr. Chande works as a Systems Engineer at Wireless Research and Development at Qualcomm Technologies Inc. His current work gives him an opportunity to participate in and witness the advances in millimeter-wave radio bands, unlicensed spectrum access and machine learning for Industrial IoT.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eConsolidating knowledge on Joint Source-Channel Coding (JSCC), this book provides an indispensable resource on a key area of performance enhancement for communications networks\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePresenting in one volume the key theories, concepts and important developments in the area of Joint Source-Channel Coding (JSCC), this book provides the fundamental material needed to enhance the performance of digital and wireless communication systems and networks. \u003c\/p\u003e\u003cp\u003eIt comprehensively introduces JSCC technologies for communications systems, including coding and decoding algorithms, and emerging applications of JSCC in current wireless communications. The book covers the full range of theoretical and technical areas before concluding with a section considering recent applications and emerging designs for JSCC. A methodical reference for academic and industrial researchers, development engineers, system engineers, system architects and software engineers, this book: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Explains how JSCC leads to high performance in communication systems and networks\u003c\/li\u003e \u003cli\u003eConsolidates key material from multiple disparate sources\u003c\/li\u003e \u003cli\u003e Is an ideal reference for graduate-level courses on digital or wireless communications, as well as courses on information theory\u003c\/li\u003e \u003cli\u003e Targets professionals involved with digital and wireless communications and networking systems\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989491728613,"sku":"NP9781119978527","price":120.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119978527.jpg?v=1761784319","url":"https:\/\/k12savings.com\/products\/joint-source-channel-coding-isbn-9781119978527","provider":"K12savings","version":"1.0","type":"link"}