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245 0 0 _aFoundations of semantic communication networks /
_cedited by Walid Saad, Christina Chaccour, Christo Kurisummoottil Thomas, Mreouane Debbah.
264 1 _aHoboken, New Jersey :
_bWiley,
_c[2025]
300 _a1 online resource (416 p.)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aElectronic book.
504 _aIncludes bibliographical references and index.
505 0 _aAbout the Editors xvii -- List of Contributors xxi -- Preface xxvii -- Acknowledgment xxix -- Acronyms xxxi -- 1 Introduction to Semantic Communications 1 Christina Chaccour, Christo Kurisummoottil Thomas, Walid Saad, and Merouane Debbah -- 1.1 From Information Streams to Streams of Understanding: The Rise of Semantic Communication Networks 1 -- 1.1.1 How Does It Work? 3 -- 1.1.2 Why Now? What Factors Contribute to Our Ongoing Reliance on Traditional Communications? 6 -- 1.1.3 What Is NOT Semantic Communications? 7 -- 1.1.3.1 Semantic Communications Is Not Data Compression 7 -- 1.1.3.2 Semantic Communications Is Not Only an "AI for Wireless" Concept 9 -- 1.1.3.3 Semantic Communications Is Not Only Goal-Oriented Communications 9 -- 1.1.3.4 Semantic Communications Is Not Only Application-Aware Communications 10 -- 1.2 Reimagining Future G Applications with Semantic Communications 11 -- 1.2.1 Semantic Communication for Next-Generation XR 11 -- 1.2.2 Digital Reality and Massive Twinning: Speaking the Same Language 13 -- 1.2.3 Semantic Communication and Sustainable Networks: A Convergence for Efficiency 14 -- 1.3 Structure and Path of the Book 16 -- Bibliography 17 -- Part I Fundamentals of Semantic Communications 19 -- 2 Semantic Compression and Communication: Fundamentals and Methodologies 21 Emrecan Kutay and Aylin Yener -- 2.1 Introduction 21 -- 2.1.1 Notation 23 -- 2.2 Semantic Index Assignment 23 -- 2.2.1 System Model 24 -- 2.2.1.1 Minimization of Semantic Distortion 25 -- 2.2.1.2 Graph Coloring Problem 26 -- 2.2.1.3 Joint Graph Coloring and Index Assignment 27 -- 2.3 The Rise of Machine Intelligence in Perception 29 -- 2.4 Semantic Compression for Multimodal Sources 32 -- 2.4.1 System Model 32 -- 2.4.1.1 Semantic Quantization 33 -- 2.4.1.2 Semantic Compression 34 -- 2.4.1.3 Semantic Vector Quantized Autoencoder 36 -- 2.4.2 Results 39 -- 2.5 Conclusion 44 -- Bibliography 44 -- 3 Toward a Theory of Semantic Information 49 Jean-Claude Belfiore and Daniel Bennequin -- 3.1 Introduction 49 -- 3.2 Cohomological Nature of Information 50 -- 3.3 Axioms for Information Spaces 52 -- 3.4 Comparison with Other Propositions of Semantic Information Measures 54 -- 3.5 Carnap and Bar-Hillel Languages 54 -- 3.6 Shepard's Experiment 57 -- Bibliography 59 -- 4 Deep Joint Source and Channel Coding 61 Haotian Wu, Chenghong Bian, Yulin, Shao, and Deniz Gündüz -- 4.1 Introduction 61 -- 4.2 DeepJSCC for MIMO Channels 62 -- 4.2.1 System Model 62 -- 4.2.1.1 Open-loop MIMO with CSIR 63 -- 4.2.1.2 Closed-loop MIMO with CSIT 64 -- 4.2.2 A DeepJSCC-MIMO Solution 65 -- 4.2.2.1 Image-to-Sequence Transformation 66 -- 4.2.2.2 Channel Heatmap Construction 66 -- 4.2.2.3 ViT Encoding 67 -- 4.2.2.4 ViT Decoding 67 -- 4.2.2.5 Loss Function 68 -- 4.2.3 Training and Evaluation 68 -- 4.2.3.1 Open-loop MIMO System with CSIR 68 -- 4.2.3.2 Closed-loop MIMO System with CSIT 71 -- 4.3 DeepJSCC for Relay Channels 75 -- 4.3.1 Cooperative Relay 75 -- 4.3.1.1 System Model 75 -- 4.3.1.2 DeepJSCC for Cooperative Relay 76 -- 4.3.1.3 Numerical Experiments 79 -- 4.3.2 Multihop Relay 81 -- 4.3.2.1 System Model 82 -- 4.3.2.2 Existing Methods 83 -- 4.3.2.3 A Hybrid JSCC Solution 84 -- 4.3.2.4 Numerical Experiments 88 -- 4.4 DeepJSCC for Feedback Channels 91 -- 4.4.1 System Model 91 -- 4.4.2 A JSCCFormer-f Solution 93 -- 4.4.2.1 ViT Encoder 93 -- 4.4.2.2 ViT Decoder 95 -- 4.4.3 Training and Evaluation 96 -- 4.4.3.1 Transmission Performance 96 -- 4.4.3.2 Impacts of Bandwidth Ratio and Block Number 97 -- 4.4.3.3 Noisy Feedback Channel 100 -- 4.4.3.4 Adaptability 100 -- 4.4.3.5 High Resolution Dataset and Visualization 102 -- 4.4.3.6 Variable Rate Transmission 102 -- 4.5 Concluding Remarks 106 -- Bibliography 107 -- 5 When Information Is a Function of Data - Some Information Theoretic Perspectives on Semantic Communications 111 Alexander Mariona, Homa Esfahanizadeh, Rafael Gregorio Lucas D'Oliveira, and Muriel Médard -- 5.1 The Central Limit Theorem 112 -- 5.2 Quantitative Bounds 113 -- 5.3 General Polynomials 115 -- 5.4 Examples and Applications 119 -- 5.4.1 The Computational Wiretap Channel 120 -- 5.5 Further Generalizations 122 -- Bibliography 123 -- 6 Interoperability and Coexistence of 6G Semantic, Goal-Oriented, and Legacy Systems 125 Emilio Calvanese Strinati, Mohamed Sana, Mattia Merluzzi, and Toms̀ Huttebraucker -- 6.1 Introduction 125 -- 6.2 Interoperability Issue in Goal-oriented and Semantic Systems 126 -- 6.2.1 Language in Multiuser Communication 128 -- 6.2.2 A Measure of Semantic Mismatch 129 -- 6.2.3 Semantic Channel Equalization 130 -- 6.3 Coexistence of Semantic, Goal-Oriented, and Legacy Services in 6G 134 -- 6.3.1 Goal-Oriented Resource Allocation 135 -- 6.3.2 Goal-Driven Measures for Edge Inference 135 -- 6.4 Conclusion 137 -- Acknowledgment 138 -- Bibliography 138 -- Part II Semantic Communications Networking 141 -- 7 Optimization of Image Transmission in a Cooperative Semantic Communication Networks 143 Ye Hu and Mingzhe Chen -- 7.1 Introduction 143 -- 7.1.1 Related Works 144 -- 7.2 Representative Work 145 -- 7.2.1 System Model 145 -- 7.2.2 Semantic Information Extraction 146 -- 7.2.3 Transmission Model 149 -- 7.2.4 Image Semantic Similarity Model 150 -- 7.2.5 Problem Formulation 151 -- 7.3 Value-Decomposition-based Entropy-Maximized Multi-Agent RL Method 152 -- 7.3.1 Components of VD-ERL Method 152 -- 7.3.2 VD-ERL Algorithm for Semantic-Oriented Resource Allocation 155 -- 7.3.3 Complexity and Convergence of the Introduced Algorithm 157 -- 7.4 Simulation Results and Analysis 158 -- 7.5 Conclusion 161 -- Bibliography 162 -- 8 Multiple Access Design for Joint Semantic and Classical Communications 165 Xidong Mu and Yuanwei Liu -- 8.1 Introduction 165 -- 8.2 Heterogeneous Semantic and Bit Multiuser Network 167 -- 8.2.1 Multiple Access for the Heterogeneous Semantic and Bit Multiuser Network 169 -- 8.2.2 Interplay Between Semantic Communications and NOMA 169 -- 8.3 NOMA-Enabled Heterogeneous Semantic and Bit Multiuser Communications 170 -- 8.3.1 Semantic Rate: A New Performance Metric 170 -- 8.3.2 Semi-NOMA: A Unified Multiple Access Scheme 171 -- 8.3.3 Fundamental Limit: Semantic-Versus-Bit Rate Region 173 -- 8.4 Semantic Communications-Enhanced NOMA 175 -- 8.4.1 Early-Late Rate Disparity Issue in NOMA 175 -- 8.4.2 An Opportunistic Semantic and Bit Communication Approach for Noma 177 -- 8.4.3 Numerical Case Studies 177 -- 8.5 Concluding Remarks and Future Research 179 -- Bibliography 179 -- 9 Contextual Reasoning-based Semantics-Native Communication 181 Hyowoon Seo, Yoon Huh, Heekang Song, Wan Choi, and Mehdi Bennis -- 9.1 Semantics-Native Communication 181 -- 9.1.1 System Model 182 -- 9.1.1.1 Information-Theoretic Model Description 183 -- 9.1.1.2 Motivation from Triangle of Meaning Model 183 -- 9.2 Contextual Reasoning for Semantics-Native Communication 184 -- 9.2.1 Motivation from Referential Game 185 -- 9.2.2 Single-Sided Contextual Reasoning 185 -- 9.2.3 Double-Sided Contextual Reasoning 188 -- 9.2.4 Multi-round Contextual Reasoning 189 -- 9.3 Context Synchronization for Semantics-Native Communication 192 -- 9.3.1 Bayesian Inverse Contextual Reasoning 193 -- 9.3.2 Inverse Linearized Contextual Reasoning 194 -- 9.3.2.1 Linearizing Contextual Reasoning 195 -- 9.3.2.2 Invertible Linearized Contextual Reasoning 196 -- 9.4 Information Bottleneck Contextual Reasoning 197 -- 9.4.1 Information Bottleneck Method 197 -- 9.4.2 Implementing Information Bottleneck with Contextual Reasoning 198 -- 9.5 Conclusion 198 -- Bibliography 199 -- 10 Interoperable Semantic Communication 201 Jinhyuk Choi, Hyelin Nam, Jihong Park, Seung-Woo Ko, Jinho Choi, Mehdi Bennis, and Seong-Lyun Kim -- 10.1 Pitfalls of Federated Learning for Semantic Alignment 201 -- 10.2 Split Learning for Semantic Alignment 203 -- 10.3 In-Context Learning for Semantic Alignment 207 -- 10.4 Conclusion and Future Directions 211 -- Bibliography 212 -- Part III Machine Reasoning for Ai-native Semantic Communication Networks 215 -- 11 Causal Reasoning Foundations of Semantic Communication Systems 217 Christo Kurisummoottil Thomas, Christina Chaccour, Walid Saad, and Merouane Debbah -- 11.1 Introduction 217 -- 11.2 Causality Primer 219 -- 11.3 Causal Semantic Communications 222 -- 11.3.1 System Model 222 -- 11.3.1.1 How to Pose the Proper Interventions and Counterfactuals via Queries? 224 -- 11.3.2 Emergent Language Model 226 -- 11.3.3 Semantic Information Measure 227 -- 11.3.4 Signaling Game Model and Generalized Nash
505 0 _aEquilibrium Problem 230 -- 11.3.5 Characterization of the Generalized Local NE 232 -- 11.3.6 Analysis of the Signaling Game Equilibria for Emergent Language 233 -- 11.3.7 Average Semantic Representation Length for Classical and Emergent Language Based ESC 235 -- 11.4 Numerical Results 236 -- 11.4.1 Illustrative Example for NeSy AI's Potential in Wireless Versus Classical AI Based Wireless 236 -- 11.5 Conclusion 239 -- Bibliography 240 -- 12 Reinforcement Learning-Based Unicast and Broadcast Wireless Semantic C ...
520 _aComprehensive overview of the principles, theories, and techniques needed to build end-to-end semantic communication systems, with case studies included. In this rapidly evolving landscape, the integration of connected intelligence applications highlights the pressing need for networks to gain intelligence in a non-siloed and ad hoc manner. The traditional incremental approach to network design is no longer sufficient to support the diverse and dynamic requirements of these emerging applications. This necessitates a paradigm shift towards more intelligent and adaptive network architectures. From theory to application, Foundations of Semantic Communication Networks describes and provides a comprehensive understanding of everything needed to build end-to-end semantic communication systems. This book covers various interdisciplinary topics such as the mathematical foundations of semantic communications, information theoretical perspectives, joint-source channel coding, semantic-aware resource management strategies, interoperability under heterogeneous semantic communication users, advanced artificial intelligence (AI) and machine reasoning techniques for enabling connected intelligent applications, secure and privacy-preserving semantic communication systems, and the coexistence and interoperability of semantic, goal-oriented, and legacy systems. The book examines unique features of end-to-end networking with semantic communications, including instilling reasoning behaviors in communication nodes, the role of the semantic plane in information filtering, control of communication and computing resources, transmit and receive signaling schemes, and connected intelligence device control. It emphasizes the importance of data semantics and age of information metrics. The book also discusses the profound impact of semantic communications on the telecom industry, highlighting changes in network performance, resource management, traffic, as well as spectral and energy efficiency. Furthermore, the book provides insights into the mathematical constructs and AI theories for formulating semantic information, such as topology and category theory. It explores real-world applications, case studies, and future research directions as wireless technologies transition to 6G and beyond. Written by four recognized experts in the field with a wealth of expertise from academia, industry, and research institutions, Foundations of Semantic Communication Networks addresses sample topics, including: Novel Semantic Information Formulations: Proposing new formulations using rigorous mathematical frameworks such as category theory and algebraic topology. Practical Applications and Networking Features: Focusing on real-world scenarios, addressing multiple access and networking challenges through collaborative frameworks for multi-modal transmissions, examining multiple access schemes to enhance transmission efficiency, and ensuring coexistence with legacy systems. AI-Native Air Interface and Semantic-Aware Resource Allocation: Enabling efficient large-scale systems for 6G and beyond wireless systems through AI-native air interfaces and semantic-aware resource allocation strategies. Advanced AI and Machine Reasoning: Utilizing causality and neuro-symbolic artificial intelligence for minimalistic transmissions, and achieving generalizability and transferability across contexts and data distributions to develop high-fidelity semantic communication systems. Multi-Domain Security Vulnerabilities: Examining security vulnerabilities associated with deep neural networks in semantic communications, and proposing encrypted, privacy-preserving semantic communication systems (ESCS) as a solution. Foundations of Semantic Communication Networks is an excellent forward-thinking resource on the subject for readers with a strong background in the subject matter, including graduate-level students, academics, practitioners, and industry researchers.
521 _aProfessional and scholarly.
588 _aDescription based on online resource; title from digital title page (viewed on March 18, 2025).
650 0 _aWireless communication systems.
_0http://id.loc.gov/authorities/subjects/sh92006740
650 0 _aSemantic networks (Information theory)
_0http://id.loc.gov/authorities/subjects/sh92004914
650 6 _aTransmission sans fil.
650 6 _aRéseaux sémantiques.
650 7 _aMobile & Wireless Communications.
_2bisacsh/2023
650 7 _aTECHNOLOGY & ENGINEERING.
_2bisacsh/2023
650 7 _aMobile & Wireless Communications.
_2bisacsh/2024
650 7 _aTECHNOLOGY & ENGINEERING.
_2bisacsh/2024
655 0 _aElectronic books.
700 1 _aSaad, Walid.
_1https://id.oclc.org/worldcat/entity/E39PCjGqRBDM4qFHHgkXhTj7pd
700 1 _aChaccour, Christina.
700 1 _aThomas, Christo Kurisummoottil.
700 1 _aDebbah, Merouane,
_d1975-
_1https://id.oclc.org/worldcat/entity/E39PBJcgrrtdg3VBR6KyYBYHG3
776 0 8 _iPrint version:
_z9781394247882
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394247912
_yFull text is available at Wiley Online Library. Click here to view.
942 _2ddc
_cER