Оглавление
Группа авторов. Shaping Future 6G Networks
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Shaping Future 6G Networks. Needs, Impacts, and Technologies
Editor Biographies
List of Contributors
Forewords. Henning Schulzrinne, Columbia University, USA
Peter Stuckmann, Head of Unit, Future Connectivity Systems, European Commission
Notes
Akihiro Nakao, The University of Tokyo, Japan
Acronyms
1 Toward 6G – Collecting the Research Visions
1.1 Time to Start Shaping 6G
1.2 Early Directions for Shaping 6G. 1.2.1 Future Services
1.2.2 Moving from 5G to 6G
1.2.3 Renewed Value Chain and Collaborations
1.3 Book Outline and Main Topics. 1.3.1 Use Cases and Requirements for 6G (Chapter 2)
1.3.2 Standardization Processes for 6G (Chapter 3)
1.3.3 Energy Consumption and Social Acceptance (Chapters 4 and 5)
1.3.4 New Technologies for Radio Access (Chapters 6–8)
1.3.5 New Technologies for Network Infrastructure (Chapters 9 and 10)
1.3.6 New Perspectives for Network Architectures (Chapters 11 and 12)
1.3.7 New Technologies for Network Management and Operation (Chapters 13–15)
1.3.8 Post‐Shannon Perspectives (Chapter 16)
2 6G Drivers for B2B Market: E2E Services and Use Cases
2.1 Introduction
2.2 Relevance of the B2B market for 6G
2.3 Use Cases for the B2B Market
2.3.1 Industry and Manufacturing
2.3.2 Teleportation
2.3.3 Digital Twin
2.3.4 Smart Transportation
2.3.5 Public Safety
2.3.6 Health and Well‐being
2.3.7 Smart‐X IoT
2.3.8 Financial World
2.4 Conclusions
References
Note
3 6G: The Path Toward Standardization
3.1 Introduction
3.2 Standardization: A Long‐Term View
3.3 IMTs Have Driven Multiple Approaches to Previous Mobile Generations
3.4 Stakeholder Ecosystem Fragmentation and Explosion
3.5 Shifting Sands: Will Politics Influence Future Standardization Activities?
3.6 Standards, the Supply Chain, and the Emergence of Open Models
3.7 New Operating Models
3.8 Research – What Is the Industry Saying?
3.9 Can We Define and Deliver a New Generation of Standards by 2030?
3.10 Conclusion
References
4 Greening 6G: New Horizons
4.1 Introduction
4.2 Energy Spreadsheet of 6G Network and Its Energy Model. 4.2.1 Radio Access Network Energy Consumption Model
4.2.2 Edge Computing and Learning: Energy Consumption Models and Their Impacts
4.2.2.1 Energy Consumption Models in Edge Computing
4.2.2.2 Energy Consumption Models in Edge Learning
4.3 Greening 6G Radio Access Networks. 4.3.1 Energy‐Efficient Network Planning
4.3.1.1 BS Deployment Densification with Directional Transmissions
4.3.1.2 Network with Reconfigurable Intelligent Surfaces (RISs)
4.3.2 Energy‐Efficient Radio Resource Management
4.3.2.1 Model‐Free
4.3.2.2 Less Computation Complexity
4.3.3 Energy‐Efficient Service Provisioning with NFV and SFC
4.3.3.1 VNF Consolidation
4.3.3.2 Exploiting Renewable Energy
4.4 Greening Artificial Intelligence (AI) in 6G Network
4.4.1 Energy‐Efficient Edge Training
4.4.2 Distributed Edge Co‐inference and the Energy Trade‐off
4.5 Conclusions
References
5 “Your 6G or Your Life”: How Can Another G Be Sustainable?
5.1 Introduction
5.2 A World in Crisis
5.2.1 Ecological Crisis
5.2.2 Energy Crises
5.2.3 Technological Innovation and Rebound Effect: A Dead End?
5.3 A Dilemma for Service Operators. 5.3.1 Incentives to Reduce Consumption: Shooting Ourselves in the Foot?
5.3.2 Incentives to Reduce Overconsumption: Practical Solutions
5.3.3 Opportunities… and Risks
5.4 A Necessary Paradigm Shift. 5.4.1 The Status Quo Is Risky, Too
5.4.2 Creating Value with 6G in the New Paradigm
5.4.3 Empowering Consumers to Achieve the “2T CO2/Year/Person” Objective
5.5 Summary and Prospects. 5.5.1 Two Drivers, Three Levels of Action
5.5.2 Which Regulation for Future Use of Technologies?
5.5.3 Hopes and Prospects for a Sustainable 6G
References
Notes
6 Catching the 6G Wave by Using Metamaterials: A Reconfigurable Intelligent Surface Paradigm*
6.1 Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces
6.1.1 Reconfigurable Intelligent Surfaces
6.2 Types of RISs, Advantages, and Limitations
6.2.1 Advantages and Limitations
6.3 Experimental Activities
6.3.1 Large Arrays of Inexpensive Antennas. 6.3.1.1 RFocus
6.3.1.2 The ScatterMIMO Prototype
6.3.2 Metasurface Approaches
6.4 RIS Research Areas and Challenges in the 6G Ecosystem
References
Note
7 Potential of THz Broadband Systems for Joint Communication, Radar, and Sensing Applications in 6G
References
8 Non‐Terrestrial Networks in 6G
8.1 Introduction
8.2 Non‐Terrestrial Networks in 5G
8.3 Innovations in Telecom Satellites
8.4 Extended Non‐Terrestrial Networks in 6G. 8.4.1 Motivation
8.4.2 Heterogeneous and Dynamic Networks in 6G
8.5 Research Challenges Toward 6G‐NTN
8.5.1 Heterogeneous Non‐Terrestrial 6G Networks
8.5.2 Required RAN Architecture in 6G to Support NTN
8.5.3 Coexistence and Spectrum Sharing
8.5.3.1 Regulatory Aspects
8.5.3.2 Techniques for Coexistence
8.5.4 Energy‐Efficient Waveforms
8.5.5 Scalable RF Carrier Bandwidth
8.6 Conclusion
References
9 Rethinking the IP Framework
9.1 Introduction
9.2 Emerging Applications and Network Requirements
9.3 State of the Art
9.4 Next‐Generation Internet Protocol Framework: Features and Capabilities
9.4.1 High‐Precision and Deterministic Services
9.4.2 Semantic and Flexible Addressing
9.4.3 ManyNets Support
9.4.4 Intrinsic Security and Privacy
9.4.5 High Throughput
9.4.6 User‐Defined Network Operations
9.5 Flexible Addressing System Example
9.6 Conclusion
References
10 Computing in the Network: The Core‐Edge Continuum in 6G Network
10.1 Introduction
10.2 A Few Stops on the Road to Programmable Networks
10.2.1 Active Networks
10.2.2 Information‐centric Networking
10.2.3 Compute‐first Networking
10.2.4 Software‐defined Networking
10.3 Beyond Softwarization and Clouderization: The Computerization of Networks
10.3.1 A New End‐to‐End Paradigm
10.3.2 Computing in the Network Basic Concepts
10.3.3 Related Impacts
10.3.3.1 The Need for Resource Discovery
10.3.3.2 Power Savings for Eco‐conscious Networking
10.3.3.3 Transport is Still Needed!
10.3.3.4 How About Security?
10.4 Computing Everywhere: The Core‐Edge Continuum
10.4.1 A Common Data Layer
10.4.2 The New Programmable Data Plane
10.4.3 Novel Architectures Using Computing in the Network
10.4.3.1 The Newest and Boldest: Quantum Networking
10.4.3.2 Creating the Tactile and the Automated Internet: FlexNGIA
10.5 Making it Real: Use Cases
10.5.1 Computing in the Data Center
10.5.1.1 Data and Flow Aggregation
10.5.1.2 Key‐value Storage and In‐network Caching
10.5.1.3 Consensus
10.5.2 Next‐generation IoT and Intelligence Everywhere
10.5.2.1 The Internet of Intelligent Things
10.5.2.2 Industrial Automation: From Factories to Farms
10.5.3 Computing Support for Networked Multimedia
10.5.3.1 Video Analytics
10.5.3.2 Extended Reality and Multimedia
10.5.4 Melding AI and Computing for Measuring and Managing the Network
10.5.4.1 Telemetry
10.5.4.2 AI/ML for Network Management
10.5.5 Network Coding
10.6 Conclusion: 6G, the Network, and Computing
Acknowledgments
References
Note
11 An Approach to Automated Multi‐domain Service Production for Future 6G Networks
11.1 Introduction. 11.1.1 Background
11.1.2 The Need for Multi‐domain 6G Networks
11.1.3 Challenges of Multi‐domain Service Production and Operation
11.2 Framework and Assumptions
11.2.1 Terminology
11.2.2 Assumptions. 11.2.2.1 SDN‐enabled Domains
11.2.2.2 On‐service Orchestrators
11.2.2.3 Any Kind of Multi‐domain Service, Whatever the Vertical
11.2.3 Roles
11.2.4 Possible Multi‐domain Service Delivery Frameworks
11.2.4.1 A Set of Bilateral Agreements
11.2.4.2 A Set of Bilateral Agreements by Means of a Marketplace
11.2.4.3 A Set of Bilateral Agreements by Means of a Broker
11.3 Automating the Delivery of Multi‐domain Services
11.3.1 General Considerations
11.3.2 Discovering Partnering Domains and Communicating with Partnering SDN Controllers
11.3.3 Multi‐domain Service Subscription Framework
11.3.4 Multi‐domain Service Delivery Procedure
11.4 An Example: Dynamic Enforcement of Differentiated, Multi‐domain Service Traffic Forwarding Policies by Means of Service Function Chaining
11.4.1 SFC Control Plane
11.4.2 Consistency of Operation
11.4.3 Design Considerations
11.5 Research Challenges
11.5.1 Security of Operations
11.5.2 Consistency of Decisions
11.5.3 Consistency of Data
11.5.4 Performance and Scalability
11.6 Conclusion
References
12 6G Access and Edge Computing – ICDT Deep Convergence
12.1 Introduction
12.2 True ICT Convergence: RAN Evolution to 5G
12.2.1 C‐RAN: Centralized, Cooperative, Cloud, and Clean
12.2.1.1 NGFI: From Backhaul to xHaul
12.2.1.2 From Cloud to Fog
12.2.2 A Turbocharged Edge: MEC
12.2.3 Virtualization and Cloud Computing
12.3 Deep ICDT Convergence Toward 6G
12.3.1 Open and Smart: Two Major Trends Since 5G
12.3.1.1 RAN Intelligence – Enabled with Wireless Big Data
12.3.1.1.1 Big Data Module Function Definition
12.3.1.1.2 Interface
12.3.1.2 OpenRAN
12.3.1.3 Scope of RAN Intelligence Use Cases
12.3.1.3.1 Energy Saving
12.3.1.3.2 Automatic Anomaly Analysis
12.3.1.3.3 Near‐real‐time QoE Optimization
12.3.1.3.4 Radio Fingerprint‐based Traffic Steering
12.3.2 An OpenRAN Architecture with Native AI: RAN Intelligent Controller (RIC)
12.3.2.1 NRT‐RIC Functions
12.3.2.2 nRT‐RIC Functions
12.3.3 Key Challenges and Potential Solutions. 12.3.3.1 Customized Data Collection and Control
12.3.3.2 Radio Resource Management and Air Interface Protocol Processing Decoupling
12.3.3.3 Open API for xApp
12.4 Ecosystem Progress from 5G to 6G
12.4.1 O‐RAN Alliance
12.4.2 Telecom Infrastructure Project
12.4.3 GSMA Open Networking Initiative
12.4.4 Open‐source Communities
12.5 Conclusion
Acknowledgments
References
13 “One Layer to Rule Them All”: Data Layer‐oriented 6G Networks
13.1 Perspective
13.2 Motivation
13.3 Requirements
13.4 Benefits/Opportunities
13.5 Data Layer High‐level Functionality
13.6 Instead of Conclusions
References
14 Long‐term Perspectives: Machine Learning for Future Wireless Networks
14.1 Introduction
14.2 Why Machine Learning in Communication?
14.2.1 Machine Learning in a Nutshell
14.2.1.1 Kernel‐based Learning with Projections
14.2.1.2 Deep Learning
14.2.1.3 Reinforcement Learning
14.2.2 Choosing the Right Tool for the Job
14.3 Machine Learning in Future Wireless Networks
14.3.1 Robust Traffic Prediction for Energy‐saving Optimization
14.3.2 Fingerprinting‐based Localization
14.3.3 Joint Power and Beam Optimization
14.3.4 Collaborative Compressive Classification
14.3.5 Designing Neural Architectures for Sparse Estimation
14.3.6 Online Loss Map Reconstruction
14.3.7 Learning Non‐Orthogonal Multiple Access and Beamforming
14.3.8 Simulating Radiative Transfer
14.4 The Soul of 6G will be Machine Learning
14.5 Conclusion
References
Notes
15 Managing the Unmanageable: How to Control Open and Distributed 6G Networks
15.1 Introduction
15.2 Managing Open and Distributed Radio Access Networks. 15.2.1 Radio Access Network
15.2.2 Innovation in the Standardization Arena. 15.2.2.1 RAN
15.3 Core Network and End‐to‐End Network Management
15.3.1 Network Architecture and Management
15.3.2 Changes in Architecture and Network Management from Standardization Perspective
15.3.3 Quality of Service and Experience
15.3.4 Standardization Effort in Data Analytics
15.4 Trends in Machine Learning Suitable to Network Data and 6G. 15.4.1 Federated Learning
15.4.2 Auto‐Labeling Techniques and Network Actuations
15.5 Conclusions
References
16 6G and the Post‐Shannon Theory
16.1 Introduction
16.2 Message Identification for Post‐Shannon Communication
16.2.1 Explicit Construction of RI Codes
16.2.2 Secrecy for Free
16.2.3 Message Identification Without Randomness
16.3 Resources Considered Useless Become Relevant. 16.3.1 Common Randomness for Nonsecure Communication
16.3.2 Feedback in Identification and the Additivity of Bundled Channels
16.4 Physical Layer Service Integration. 16.4.1 Motivation and Requirements
16.4.2 Detectability of Denial‐of‐Service Attacks
16.4.3 Further Limits for Computer‐Aided Approaches
16.5 Other Implementations of Post‐Shannon Communication
16.5.1 Post‐Shannon in Multi‐Code CDMA
16.5.2 Waveform Coding in MIMO Systems
16.6 Conclusions: A Call to Academia and Standardization Bodies
Acknowledgments
References
Index
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