Shaping Future 6G Networks

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 1
Emmanuel 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 9
Marco 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 23
Guy 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 39
Zhisheng 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? 55
Isabelle 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 69
Marco 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 89
Robert Müller and Markus Landmann

8 Non-Terrestrial Networks in 6G 101
Thomas 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 117
David 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 133
Marie-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 167
Mohamed 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 187
Chih-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 O-RAN Alliance 214

12.4.2 Telecom Infrastructure Project 215

12.4.3 GSMA Open Networking Initiative 216

12.4.4 Open-source Communities 216

12.5 Conclusion 217

13 “One Layer to Rule Them All”: Data Layer-oriented 6G Networks 221
Marius Corici and Thomas Magedanz

13.1 Perspective 221

13.2 Motivation 222

13.3 Requirements 223

13.4 Benefits/Opportunities 225

13.5 Data Layer High-level Functionality 227

13.6 Instead of Conclusions 231

14 Long-term Perspectives: Machine Learning for Future Wireless Networks 235
Sławomir Stańczak, Alexander Keller, Renato L.G. Cavalcante, Nikolaus Binder, and Soma Velayutham

14.1 Introduction 235

14.2 Why Machine Learning in Communication? 236

14.2.1 Machine Learning in a Nutshell 237

14.2.1.1 Kernel-based Learning with Projections 237

14.2.1.2 Deep Learning 238

14.2.1.3 Reinforcement Learning 241

14.2.2 Choosing the Right Tool for the Job 242

14.3 Machine Learning in Future Wireless Networks 243

14.3.1 Robust Traffic Prediction for Energy-saving Optimization 244

14.3.2 Fingerprinting-based Localization 244

14.3.3 Joint Power and Beam Optimization 245

14.3.4 Collaborative Compressive Classification 245

14.3.5 Designing Neural Architectures for Sparse Estimation 247

14.3.6 Online Loss Map Reconstruction 248

14.3.7 Learning Non-Orthogonal Multiple Access and Beamforming 248

14.3.8 Simulating Radiative Transfer 250

14.4 The Soul of 6G will be Machine Learning 251

14.5 Conclusion 252

15 Managing the Unmanageable: How to Control Open and Distributed 6G Networks 255
Imen Grida Ben Yahia, Zwi Altman, Joanna Balcerzak, Yosra Ben Slimen, and Emmanuel Bertin

15.1 Introduction 255

15.2 Managing Open and Distributed Radio Access Networks 256

15.2.1 Radio Access Network 256

15.2.2 Innovation in the Standardization Arena 258

15.2.2.1 RAN 258

15.3 Core Network and End-to- End Network Management 260

15.3.1 Network Architecture and Management 260

15.3.2 Changes in Architecture and Network Management from Standardization Perspective 262

15.3.3 Quality of Service and Experience 263

15.3.4 Standardization Effort in Data Analytics 264

15.4 Trends in Machine Learning Suitable to Network Data and 6G 265

15.4.1 Federated Learning 265

15.4.2 Auto-Labeling Techniques and Network Actuations 266

15.5 Conclusions 268

16 6G and the Post-Shannon Theory 271
Juan A. Cabrera, Holger Boche, Christian Deppe, Rafael F. Schaefer, Christian Scheunert, and Frank H. P. Fitzek

16.1 Introduction 271

16.2 Message Identification for Post-Shannon Communication 273

16.2.1 Explicit Construction of RI Codes 277

16.2.2 Secrecy for Free 279

16.2.3 Message Identification Without Randomness 280

16.3 Resources Considered Useless Become Relevant 281

16.3.1 Common Randomness for Nonsecure Communication 281

16.3.2 Feedback in Identification and the Additivity of Bundled Channels 282

16.4 Physical Layer Service Integration 283

16.4.1 Motivation and Requirements 283

16.4.2 Detectability of Denial-of-Service Attacks 284

16.4.3 Further Limits for Computer-Aided Approaches 288

16.5 Other Implementations of Post-Shannon Communication 288

16.5.1 Post-Shannon in Multi-Code CDMA 288

16.5.2 Waveform Coding in MIMO Systems 289

16.6 Conclusions: A Call to Academia and Standardization Bodies 290

Index 295