Shaping Future 6G Networks
Needs, Impacts, and Technologies
Wiley - IEEE
1. Auflage Dezember 2021
336 Seiten, Hardcover
Fachbuch
ISBN:
978-1-119-76551-6
John Wiley & Sons
Weitere Versionen
Shaping Future 6G Networks
Discover the societal and technology drivers contributing to build the next generation of wireless telecommunication networks
Shaping Future 6G Networks: Needs, Impacts, and Technologies is a holistic snapshot on the evolution of 5G technologies towards 6G. With contributions from international key players in industry and academia, the book presents the hype versus the realistic capabilities of 6G technologies, and delivers cutting-edge business and technological insights into the future wireless telecommunications landscape.
You'll learn about:
* Forthcoming demand for post 5G networks, including new requirements coming from small and large businesses, manufacturing, logistics, and automotive industry
* Societal implications of 6G, including digital sustainability, strategies for increasing energy efficiency, as well as future open networking ecosystems
* Impacts of integrating non-terrestrial networks to build the 6G architecture
* Opportunities for emerging THz radio access technologies in future integrated communications, positioning, and sensing capabilities in 6G
* Design of highly modular and distributed 6G core networks driven by the ongoing RAN-Core integration and the benefits of AI/ML-based control and management
* Disruptive architectural considerations influenced by the Post-Shannon Theory
The insights in Shaping Future 6G Networks will greatly benefit IT engineers and managers focused on the future of networking, as well as undergraduate and graduate engineering students focusing on the design, implementation, and management of mobile networks and applications.
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
SBawomir Stanczak, 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
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
SBawomir Stanczak, 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
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.
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.
