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 xiiiList of Contributors xvForeword Henning Schulzrinne xixForeword Peter Stuckmann xxiForeword Akihiro Nakao xxiiiAcronyms xxv1 Toward 6G – Collecting the Research Visions 1Emmanuel Bertin, Thomas Magedanz, and Noel Crespi1.1 Time to Start Shaping 6G 11.2 Early Directions for Shaping 6G 21.2.1 Future Services 21.2.2 Moving from 5G to 6G 21.2.3 Renewed Value Chain and Collaborations 31.3 Book Outline and Main Topics 41.3.1 Use Cases and Requirements for 6G 41.3.2 Standardization Processes for 6G 41.3.3 Energy Consumption and Social Acceptance 41.3.4 New Technologies for Radio Access 51.3.5 New Technologies for Network Infrastructure 51.3.6 New Perspectives for Network Architectures 61.3.7 New Technologies for Network Management and Operation 71.3.8 Post-Shannon Perspectives 82 6G Drivers for B2B Market: E2E Services and Use Cases 9Marco Giordani, Michele Polese, Andres Laya, Emmanuel Bertin, and Michele Zorzi2.1 Introduction 92.2 Relevance of the B2B market for 6G 102.3 Use Cases for the B2B Market 112.3.1 Industry and Manufacturing 112.3.2 Teleportation 132.3.3 Digital Twin 152.3.4 Smart Transportation 152.3.5 Public Safety 162.3.6 Health and Well-being 172.3.7 Smart-X IoT 192.3.8 Financial World 202.4 Conclusions 223 6G: The Path Toward Standardization 23Guy Redmill and Emmanuel Bertin3.1 Introduction 233.2 Standardization: A Long-Term View 243.3 IMTs Have Driven Multiple Approaches to Previous Mobile Generations 253.4 Stakeholder Ecosystem Fragmentation and Explosion 263.5 Shifting Sands: Will Politics Influence Future Standardization Activities? 283.6 Standards, the Supply Chain, and the Emergence of Open Models 303.7 New Operating Models 323.8 Research – What Is the Industry Saying? 333.9 Can We Define and Deliver a New Generation of Standards by 2030? 343.10 Conclusion 344 Greening 6G: New Horizons 39Zhisheng Niu, Sheng Zhou, and Noel Crespi4.1 Introduction 394.2 Energy Spreadsheet of 6G Network and Its Energy Model 404.2.1 Radio Access Network Energy Consumption Model 404.2.2 Edge Computing and Learning: Energy Consumption Models and Their Impacts 414.2.2.1 Energy Consumption Models in Edge Computing 414.2.2.2 Energy Consumption Models in Edge Learning 414.3 Greening 6G Radio Access Networks 424.3.1 Energy-Efficient Network Planning 424.3.1.1 BS Deployment Densification with Directional Transmissions 424.3.1.2 Network with Reconfigurable Intelligent Surfaces (RISs) 434.3.2 Energy-Efficient Radio Resource Management 444.3.2.1 Model-free 444.3.2.2 Less Computation Complexity 444.3.3 Energy-Efficient Service Provisioning with NFV and SFC 464.3.3.1 VNF Consolidation 474.3.3.2 Exploiting Renewable Energy 474.4 Greening Artificial Intelligence (AI) in 6G Network 474.4.1 Energy-Efficient Edge Training 484.4.2 Distributed Edge Co-inference and the Energy Trade-off 494.5 Conclusions 505 “Your 6G or Your Life”: How Can Another G Be Sustainable? 55Isabelle Dabadie, Marc Vautier, and Emmanuel Bertin5.1 Introduction 555.2 A World in Crisis 565.2.1 Ecological Crisis 565.2.2 Energy Crises 575.2.3 Technological Innovation and Rebound Effect: A Dead End? 575.3 A Dilemma for Service Operators 595.3.1 Incentives to Reduce Consumption: Shooting Ourselves in the Foot? 595.3.2 Incentives to Reduce Overconsumption: Practical Solutions 605.3.3 Opportunities. . . and Risks 615.4 A Necessary Paradigm Shift 625.4.1 The Status Quo Is Risky, Too 625.4.2 Creating Value with 6G in the New Paradigm 635.4.3 Empowering Consumers to Achieve the “2T CO2/Year/Person” Objective 645.5 Summary and Prospects 645.5.1 Two Drivers, Three Levels of Action 645.5.2 Which Regulation for Future Use of Technologies? 655.5.3 Hopes and Prospects for a Sustainable 6G 656 Catching the 6G Wave by Using Metamaterials: A Reconfigurable Intelligent Surface Paradigm 69Marco Di Renzo and Alexis I. Aravanis6.1 Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces 696.1.1 Reconfigurable Intelligent Surfaces 706.2 Types of RISs, Advantages, and Limitations 726.2.1 Advantages and Limitations 746.3 Experimental Activities 786.3.1 Large Arrays of Inexpensive Antennas 786.3.1.1 RFocus 786.3.1.2 The ScatterMIMO Prototype 796.3.2 Metasurface Approaches 806.4 RIS Research Areas and Challenges in the 6G Ecosystem 827 Potential of THz Broadband Systems for Joint Communication, Radar, and Sensing Applications in 6G 89Robert Müller and Markus Landmann8 Non-Terrestrial Networks in 6G 101Thomas Heyn, Alexander Hofmann, Sahana Raghunandan, and Leszek Raschkowski8.1 Introduction 1018.2 Non-Terrestrial Networks in 5G 1018.3 Innovations in Telecom Satellites 1038.4 Extended Non-Terrestrial Networks in 6G 1058.4.1 Motivation 1058.4.2 Heterogeneous and Dynamic Networks in 6G 1078.5 Research Challenges Toward 6G-NTN 1078.5.1 Heterogeneous Non-Terrestrial 6G Networks 1098.5.2 Required RAN Architecture in 6G to Support NTN 1098.5.3 Coexistence and Spectrum Sharing 1108.5.3.1 Regulatory Aspects 1118.5.3.2 Techniques for Coexistence 1118.5.4 Energy-Efficient Waveforms 1128.5.5 Scalable RF Carrier Bandwidth 1138.6 Conclusion 1149 Rethinking the IP Framework 117David Zhe Luo and Noel Crespi9.1 Introduction 1179.2 Emerging Applications and Network Requirements 1189.3 State of the Art 1209.4 Next-Generation Internet Protocol Framework: Features and Capabilities 1229.4.1 High-Precision and Deterministic Services 1229.4.2 Semantic and Flexible Addressing 1249.4.3 ManyNets Support 1259.4.4 Intrinsic Security and Privacy 1269.4.5 High Throughput 1269.4.6 User-Defined Network Operations 1279.5 Flexible Addressing System Example 1279.6 Conclusion 12910 Computing in the Network: The Core-Edge Continuum in 6G Network 133Marie-José Montpetit and Noel Crespi10.1 Introduction 13310.2 A Few Stops on the Road to Programmable Networks 13410.2.1 Active Networks 13410.2.2 Information-centric Networking 13510.2.3 Compute-first Networking 13510.2.4 Software-defined Networking 13610.3 Beyond Softwarization and Clouderization: The Computerization of Networks 13710.3.1 A New End-to-End Paradigm 13710.3.2 Computing in the Network Basic Concepts 13810.3.3 Related Impacts 14010.3.3.1 The Need for Resource Discovery 14010.3.3.2 Power Savings for Eco-conscious Networking 14110.3.3.3 Transport is Still Needed! 14110.3.3.4 How About Security? 14110.4 Computing Everywhere: The Core-Edge Continuum 14310.4.1 A Common Data Layer 14310.4.2 The New Programmable Data Plane 14510.4.3 Novel Architectures Using Computing in the Network 14710.4.3.1 The Newest and Boldest: Quantum Networking 14810.4.3.2 Creating the Tactile and the Automated Internet: FlexNGIA 14810.5 Making it Real: Use Cases 14910.5.1 Computing in the Data Center 15010.5.1.1 Data and Flow Aggregation 15010.5.1.2 Key-value Storage and In-network Caching 15110.5.1.3 Consensus 15110.5.2 Next-generation IoT and Intelligence Everywhere 15210.5.2.1 The Internet of Intelligent Things 15210.5.2.2 Industrial Automation: From Factories to Farms 15310.5.3 Computing Support for Networked Multimedia 15410.5.3.1 Video Analytics 15410.5.3.2 Extended Reality and Multimedia 15410.5.4 Melding AI and Computing for Measuring and Managing the Network 15510.5.4.1 Telemetry 15510.5.4.2 AI/ML for Network Management 15610.5.5 Network Coding 15710.6 Conclusion: 6G, the Network, and Computing 15811 An Approach to Automated Multi-domain Service Production for Future 6G Networks 167Mohamed Boucadair, Christian Jacquenet, and Emmanuel Bertin11.1 Introduction 16711.1.1 Background 16711.1.2 The Need for Multi-domain 6G Networks 16811.1.3 Challenges of Multi-domain Service Production and Operation 16911.2 Framework and Assumptions 17011.2.1 Terminology 17011.2.2 Assumptions 17111.2.2.1 SDN-enabled Domains 17111.2.2.2 On-service Orchestrators 17211.2.2.3 Any Kind of Multi-domain Service, Whatever the Vertical 17211.2.3 Roles 17311.2.4 Possible Multi-domain Service Delivery Frameworks 17411.2.4.1 A Set of Bilateral Agreements 17411.2.4.2 A Set of Bilateral Agreements by Means of a Marketplace 17411.2.4.3 A Set of Bilateral Agreements by Means of a Broker 17511.3 Automating the Delivery of Multi-domain Services 17511.3.1 General Considerations 17511.3.2 Discovering Partnering Domains and Communicating with Partnering SDN Controllers 17611.3.3 Multi-domain Service Subscription Framework 17811.3.4 Multi-domain Service Delivery Procedure 17911.4 An Example: Dynamic Enforcement of Differentiated, Multi-domainService Traffic Forwarding Policies by Means of Service Function Chaining 18111.4.1 SFC Control Plane 18111.4.2 Consistency of Operation 18211.4.3 Design Considerations 18211.5 Research Challenges 18311.5.1 Security of Operations 18411.5.2 Consistency of Decisions 18411.5.3 Consistency of Data 18411.5.4 Performance and Scalability 18511.6 Conclusion 18512 6G Access and Edge Computing – ICDT Deep Convergence 187Chih-Lin I, Jinri Huang, and Noel Crespi12.1 Introduction 18712.2 True ICT Convergence: RAN Evolution to 5G 18712.2.1 C-RAN: Centralized, Cooperative, Cloud, and Clean 19012.2.1.1 NGFI: From Backhaul to xHaul 19112.2.1.2 From Cloud to Fog 19412.2.2 A Turbocharged Edge: MEC 19512.2.3 Virtualization and Cloud Computing 19712.3 Deep ICDT Convergence Toward 6G 19812.3.1 Open and Smart: Two Major Trends Since 5G 19812.3.1.1 RAN Intelligence – Enabled with Wireless Big Data 19912.3.1.2 OpenRAN 20212.3.1.3 Scope of RAN Intelligence Use Cases 20512.3.2 An OpenRAN Architecture with Native AI: RAN Intelligent Controller (RIC) 20812.3.2.1 NRT-RIC Functions 20912.3.2.2 nRT-RIC Functions 21112.3.3 Key Challenges and Potential Solutions 21212.3.3.1 Customized Data Collection and Control 21212.3.3.2 Radio Resource Management and Air Interface Protocol Processing Decoupling 21312.3.3.3 Open API for xApp 21412.4 Ecosystem Progress from 5G to 6G 21412.4.1 O-RAN Alliance 21412.4.2 Telecom Infrastructure Project 21512.4.3 GSMA Open Networking Initiative 21612.4.4 Open-source Communities 21612.5 Conclusion 21713 “One Layer to Rule Them All”: Data Layer-oriented 6G Networks 221Marius Corici and Thomas Magedanz13.1 Perspective 22113.2 Motivation 22213.3 Requirements 22313.4 Benefits/Opportunities 22513.5 Data Layer High-level Functionality 22713.6 Instead of Conclusions 23114 Long-term Perspectives: Machine Learning for Future Wireless Networks 235Sławomir Stańczak, Alexander Keller, Renato L.G. Cavalcante, Nikolaus Binder, and Soma Velayutham14.1 Introduction 23514.2 Why Machine Learning in Communication? 23614.2.1 Machine Learning in a Nutshell 23714.2.1.1 Kernel-based Learning with Projections 23714.2.1.2 Deep Learning 23814.2.1.3 Reinforcement Learning 24114.2.2 Choosing the Right Tool for the Job 24214.3 Machine Learning in Future Wireless Networks 24314.3.1 Robust Traffic Prediction for Energy-saving Optimization 24414.3.2 Fingerprinting-based Localization 24414.3.3 Joint Power and Beam Optimization 24514.3.4 Collaborative Compressive Classification 24514.3.5 Designing Neural Architectures for Sparse Estimation 24714.3.6 Online Loss Map Reconstruction 24814.3.7 Learning Non-Orthogonal Multiple Access and Beamforming 24814.3.8 Simulating Radiative Transfer 25014.4 The Soul of 6G will be Machine Learning 25114.5 Conclusion 25215 Managing the Unmanageable: How to Control Open and Distributed 6G Networks 255Imen Grida Ben Yahia, Zwi Altman, Joanna Balcerzak, Yosra Ben Slimen, and Emmanuel Bertin15.1 Introduction 25515.2 Managing Open and Distributed Radio Access Networks 25615.2.1 Radio Access Network 25615.2.2 Innovation in the Standardization Arena 25815.2.2.1 RAN 25815.3 Core Network and End-to- End Network Management 26015.3.1 Network Architecture and Management 26015.3.2 Changes in Architecture and Network Management from Standardization Perspective 26215.3.3 Quality of Service and Experience 26315.3.4 Standardization Effort in Data Analytics 26415.4 Trends in Machine Learning Suitable to Network Data and 6G 26515.4.1 Federated Learning 26515.4.2 Auto-Labeling Techniques and Network Actuations 26615.5 Conclusions 26816 6G and the Post-Shannon Theory 271Juan A. Cabrera, Holger Boche, Christian Deppe, Rafael F. Schaefer, Christian Scheunert, and Frank H. P. Fitzek16.1 Introduction 27116.2 Message Identification for Post-Shannon Communication 27316.2.1 Explicit Construction of RI Codes 27716.2.2 Secrecy for Free 27916.2.3 Message Identification Without Randomness 28016.3 Resources Considered Useless Become Relevant 28116.3.1 Common Randomness for Nonsecure Communication 28116.3.2 Feedback in Identification and the Additivity of Bundled Channels 28216.4 Physical Layer Service Integration 28316.4.1 Motivation and Requirements 28316.4.2 Detectability of Denial-of-Service Attacks 28416.4.3 Further Limits for Computer-Aided Approaches 28816.5 Other Implementations of Post-Shannon Communication 28816.5.1 Post-Shannon in Multi-Code CDMA 28816.5.2 Waveform Coding in MIMO Systems 28916.6 Conclusions: A Call to Academia and Standardization Bodies 290Index 295