Emmanuel Bertin, PhD, is a Senior Expert at Orange Innovation, France and an Adjunct Professor at Institut Polytechnique de Paris, France. His focus is on the digital transformation of networking, as well as on the associated organizational challenges.

Noel Crespi, PhD, is Professor and Head of Laboratory at the Telecom SudParis, Institut Polytechnique de Paris, France. His focus is on softwarization and Artificial Intelligence.

Thomas Magedanz, PhD, is University Professor at Technische Universität Berlin and Director of the Software-based Networks Department at Fraunhofer FOKUS in Berlin, Germany. His research focus is on software-based networking and open wireless research testbeds.

Editor Biographies xiii List of Contributors xv Foreword Henning Schulzrinne xix Foreword Peter Stuckmann xxi Foreword Akihiro Nakao xxiii Acronyms xxv 1 Toward 6G - Collecting the Research Visions 1Emmanuel Bertin, Thomas Magedanz, and Noel Crespi 1.1 Time to Start Shaping 6G 1 1.2 Early Directions for Shaping 6G 2 1.2.1 Future Services 2 1.2.2 Moving from 5G to 6G 2 1.2.3 Renewed Value Chain and Collaborations 3 1.3 Book Outline and Main Topics 4 1.3.1 Use Cases and Requirements for 6G 4 1.3.2 Standardization Processes for 6G 4 1.3.3 Energy Consumption and Social Acceptance 4 1.3.4 New Technologies for Radio Access 5 1.3.5 New Technologies for Network Infrastructure 5 1.3.6 New Perspectives for Network Architectures 6 1.3.7 New Technologies for Network Management and Operation 7 1.3.8 Post-Shannon Perspectives 8 2 6G Drivers for B2B Market: E2E Services and Use Cases 9Marco Giordani, Michele Polese, Andres Laya, Emmanuel Bertin, and Michele Zorzi 2.1 Introduction 9 2.2 Relevance of the B2B market for 6G 10 2.3 Use Cases for the B2B Market 11 2.3.1 Industry and Manufacturing 11 2.3.2 Teleportation 13 2.3.3 Digital Twin 15 2.3.4 Smart Transportation 15 2.3.5 Public Safety 16 2.3.6 Health and Well-being 17 2.3.7 Smart-X IoT 19 2.3.8 Financial World 20 2.4 Conclusions 22 3 6G: The Path Toward Standardization 23Guy Redmill and Emmanuel Bertin 3.1 Introduction 23 3.2 Standardization: A Long-Term View 24 3.3 IMTs Have Driven Multiple Approaches to Previous Mobile Generations 25 3.4 Stakeholder Ecosystem Fragmentation and Explosion 26 3.5 Shifting Sands: Will Politics Influence Future Standardization Activities? 28 3.6 Standards, the Supply Chain, and the Emergence of Open Models 30 3.7 New Operating Models 32 3.8 Research - What Is the Industry Saying? 33 3.9 Can We Define and Deliver a New Generation of Standards by 2030? 34 3.10 Conclusion 34 4 Greening 6G: New Horizons 39Zhisheng Niu, Sheng Zhou, and Noel Crespi 4.1 Introduction 39 4.2 Energy Spreadsheet of 6G Network and Its Energy Model 40 4.2.1 Radio Access Network Energy Consumption Model 40 4.2.2 Edge Computing and Learning: Energy Consumption Models and Their Impacts 41 4.2.2.1 Energy Consumption Models in Edge Computing 41 4.2.2.2 Energy Consumption Models in Edge Learning 41 4.3 Greening 6G Radio Access Networks 42 4.3.1 Energy-Efficient Network Planning 42 4.3.1.1 BS Deployment Densification with Directional Transmissions 42 4.3.1.2 Network with Reconfigurable Intelligent Surfaces (RISs) 43 4.3.2 Energy-Efficient Radio Resource Management 44 4.3.2.1 Model-free 44 4.3.2.2 Less Computation Complexity 44 4.3.3 Energy-Efficient Service Provisioning with NFV and SFC 46 4.3.3.1 VNF Consolidation 47 4.3.3.2 Exploiting Renewable Energy 47 4.4 Greening Artificial Intelligence (AI) in 6G Network 47 4.4.1 Energy-Efficient Edge Training 48 4.4.2 Distributed Edge Co-inference and the Energy Trade-off 49 4.5 Conclusions 50 5 "Your 6G or Your Life": How Can Another G Be Sustainable? 55Isabelle Dabadie, Marc Vautier, and Emmanuel Bertin 5.1 Introduction 55 5.2 A World in Crisis 56 5.2.1 Ecological Crisis 56 5.2.2 Energy Crises 57 5.2.3 Technological Innovation and Rebound Effect: A Dead End? 57 5.3 A Dilemma for Service Operators 59 5.3.1 Incentives to Reduce Consumption: Shooting Ourselves in the Foot? 59 5.3.2 Incentives to Reduce Overconsumption: Practical Solutions 60 5.3.3 Opportunities. . . and Risks 61 5.4 A Necessary Paradigm Shift 62 5.4.1 The Status Quo Is Risky, Too 62 5.4.2 Creating Value with 6G in the New Paradigm 63 5.4.3 Empowering Consumers to Achieve the "2T CO2/Year/Person" Objective 64 5.5 Summary and Prospects 64 5.5.1 Two Drivers, Three Levels of Action 64 5.5.2 Which Regulation for Future Use of Technologies? 65 5.5.3 Hopes and Prospects for a Sustainable 6G 65 6 Catching the 6G Wave by Using Metamaterials: A Reconfigurable Intelligent Surface Paradigm 69Marco Di Renzo and Alexis I. Aravanis 6.1 Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces 69 6.1.1 Reconfigurable Intelligent Surfaces 70 6.2 Types of RISs, Advantages, and Limitations 72 6.2.1 Advantages and Limitations 74 6.3 Experimental Activities 78 6.3.1 Large Arrays of Inexpensive Antennas 78 6.3.1.1 RFocus 78 6.3.1.2 The ScatterMIMO Prototype 79 6.3.2 Metasurface Approaches 80 6.4 RIS Research Areas and Challenges in the 6G Ecosystem 82 7 Potential of THz Broadband Systems for Joint Communication, Radar, and Sensing Applications in 6G 89Robert Müller and Markus Landmann 8 Non-Terrestrial Networks in 6G 101Thomas Heyn, Alexander Hofmann, Sahana Raghunandan, and Leszek Raschkowski 8.1 Introduction 101 8.2 Non-Terrestrial Networks in 5G 101 8.3 Innovations in Telecom Satellites 103 8.4 Extended Non-Terrestrial Networks in 6G 105 8.4.1 Motivation 105 8.4.2 Heterogeneous and Dynamic Networks in 6G 107 8.5 Research Challenges Toward 6G-NTN 107 8.5.1 Heterogeneous Non-Terrestrial 6G Networks 109 8.5.2 Required RAN Architecture in 6G to Support NTN 109 8.5.3 Coexistence and Spectrum Sharing 110 8.5.3.1 Regulatory Aspects 111 8.5.3.2 Techniques for Coexistence 111 8.5.4 Energy-Efficient Waveforms 112 8.5.5 Scalable RF Carrier Bandwidth 113 8.6 Conclusion 114 9 Rethinking the IP Framework 117David Zhe Luo and Noel Crespi 9.1 Introduction 117 9.2 Emerging Applications and Network Requirements 118 9.3 State of the Art 120 9.4 Next-Generation Internet Protocol Framework: Features and Capabilities 122 9.4.1 High-Precision and Deterministic Services 122 9.4.2 Semantic and Flexible Addressing 124 9.4.3 ManyNets Support 125 9.4.4 Intrinsic Security and Privacy 126 9.4.5 High Throughput 126 9.4.6 User-Defined Network Operations 127 9.5 Flexible Addressing System Example 127 9.6 Conclusion 129 10 Computing in the Network: The Core-Edge Continuum in 6G Network 133Marie-José Montpetit and Noel Crespi 10.1 Introduction 133 10.2 A Few Stops on the Road to Programmable Networks 134 10.2.1 Active Networks 134 10.2.2 Information-centric Networking 135 10.2.3 Compute-first Networking 135 10.2.4 Software-defined Networking 136 10.3 Beyond Softwarization and Clouderization: The Computerization of Networks 137 10.3.1 A New End-to-End Paradigm 137 10.3.2 Computing in the Network Basic Concepts 138 10.3.3 Related Impacts 140 10.3.3.1 The Need for Resource Discovery 140 10.3.3.2 Power Savings for Eco-conscious Networking 141 10.3.3.3 Transport is Still Needed! 141 10.3.3.4 How About Security? 141 10.4 Computing Everywhere: The Core-Edge Continuum 143 10.4.1 A Common Data Layer 143 10.4.2 The New Programmable Data Plane 145 10.4.3 Novel Architectures Using Computing in the Network 147 10.4.3.1 The Newest and Boldest: Quantum Networking 148 10.4.3.2 Creating the Tactile and the Automated Internet: FlexNGIA 148 10.5 Making it Real: Use Cases 149 10.5.1 Computing in the Data Center 150 10.5.1.1 Data and Flow Aggregation 150 10.5.1.2 Key-value Storage and In-network Caching 151 10.5.1.3 Consensus 151 10.5.2 Next-generation IoT and Intelligence Everywhere 152 10.5.2.1 The Internet of Intelligent Things 152 10.5.2.2 Industrial Automation: From Factories to Farms 153 10.5.3 Computing Support for Networked Multimedia 154 10.5.3.1 Video Analytics 154 10.5.3.2 Extended Reality and Multimedia 154 10.5.4 Melding AI and Computing for Measuring and Managing the Network 155 10.5.4.1 Telemetry 155 10.5.4.2 AI/ML for Network Management 156 10.5.5 Network Coding 157 10.6 Conclusion: 6G, the Network, and Computing 158 11 An Approach to Automated Multi-domain Service Production for Future 6G Networks 167Mohamed Boucadair, Christian Jacquenet, and Emmanuel Bertin 11.1 Introduction 167 11.1.1 Background 167 11.1.2 The Need for Multi-domain 6G Networks 168 11.1.3 Challenges of Multi-domain Service Production and Operation 169 11.2 Framework and Assumptions 170 11.2.1 Terminology 170 11.2.2 Assumptions 171 11.2.2.1 SDN-enabled Domains 171 11.2.2.2 On-service Orchestrators 172 11.2.2.3 Any Kind of Multi-domain Service, Whatever the Vertical 172 11.2.3 Roles 173 11.2.4 Possible Multi-domain Service Delivery Frameworks 174 11.2.4.1 A Set of Bilateral Agreements 174 11.2.4.2 A Set of Bilateral Agreements by Means of a Marketplace 174 11.2.4.3 A Set of Bilateral Agreements by Means of a Broker 175 11.3 Automating the Delivery of Multi-domain Services 175 11.3.1 General Considerations 175 11.3.2 Discovering Partnering Domains and Communicating with Partnering SDN Controllers 176 11.3.3 Multi-domain Service Subscription Framework 178 11.3.4 Multi-domain Service Delivery Procedure 179 11.4 An Example: Dynamic Enforcement of Differentiated, Multi-domainService Traffic Forwarding Policies by Means of Service Function Chaining 181 11.4.1 SFC Control Plane 181 11.4.2 Consistency of Operation 182 11.4.3 Design Considerations 182 11.5 Research Challenges 183 11.5.1 Security of Operations 184 11.5.2 Consistency of Decisions 184 11.5.3 Consistency of Data 184 11.5.4 Performance and Scalability 185 11.6 Conclusion 185 12 6G Access and Edge Computing - ICDT Deep Convergence 187Chih-Lin I, Jinri Huang, and Noel Crespi 12.1 Introduction 187 12.2 True ICT Convergence: RAN Evolution to 5G 187 12.2.1 C-RAN: Centralized, Cooperative, Cloud, and Clean 190 12.2.1.1 NGFI: From Backhaul to xHaul 191 12.2.1.2 From Cloud to Fog 194 12.2.2 A Turbocharged Edge: MEC 195 12.2.3 Virtualization and Cloud Computing 197 12.3 Deep ICDT Convergence Toward 6G 198 12.3.1 Open and Smart: Two Major Trends Since 5G 198 12.3.1.1 RAN Intelligence - Enabled with Wireless Big Data 199 12.3.1.2 OpenRAN 202 12.3.1.3 Scope of RAN Intelligence Use Cases 205 12.3.2 An OpenRAN Architecture with Native AI: RAN Intelligent Controller (RIC) 208 12.3.2.1 NRT-RIC Functions 209 12.3.2.2 nRT-RIC Functions 211 12.3.3 Key Challenges and Potential Solutions 212 12.3.3.1 Customized Data Collection and Control 212 12.3.3.2 Radio Resource Management and Air Interface Protocol Processing Decoupling 213 12.3.3.3 Open API for xApp 214 12.4 Ecosystem Progress from 5G to 6G 214 12.4.1...