Shaping Future 6G Networks: Needs, Impacts, and Technologies

Cover
Title Page
Copyright Page
Contents
Editor Biographies
List of Contributors
Foreword Henning Schulzrinne
Foreword Peter Stuckmann
Foreword Akihiro Nakao
Acronyms
Chapter 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
		1.3.2 Standardization Processes for 6G
		1.3.3 Energy Consumption and Social Acceptance
		1.3.4 New Technologies for Radio Access
		1.3.5 New Technologies for Network Infrastructure
		1.3.6 New Perspectives for Network Architectures
		1.3.7 New Technologies for Network Management and Operation
		1.3.8 Post-Shannon Perspectives
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 7 Potential of THz Broadband Systems for Joint Communication, Radar, and Sensing Applications in 6G
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
			12.3.1.2 OpenRAN
			12.3.1.3 Scope of RAN Intelligence Use Cases
		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
Chapter 13 "One Layer to Rule Them All": Data Layer-oriented6G 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
Chapter 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
Chapter 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
Chapter 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
Index
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