Digital Twins for 6G: Fundamental theory, technology and applications (Telecommunications)

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Contents
About the editors
Preface
1 Digital twins for resilient and reliable 6G networks
	1.1 Introduction
		1.1.1 6G KPIs
		1.1.2 6G technologies
		1.1.3 6G applications
	1.2 DTs and 6G
		1.2.1 DTs and higher frequency technologies
		1.2.2 DTs of non-terrestrial networks
		1.2.3 New physical layer and multi-antenna techniques
		1.2.4 DT and new network technologies
		1.2.5 DT and intelligent networks (AI-based networking)
	1.3 DT and Internet of Things (IoT)
	1.4 Low latency DT
		1.4.1 Low latency communications
	1.5 DT deployment challenges
	1.6 Conclusion
	References
2 Digital twin-enabled aerial edge networks with ultra-reliable low-latency communications
	2.1 Introduction
		2.1.1 Literature review
		2.1.2 Motivations and main contributions
	2.2 System model and problem formulation
		2.2.1 DT-empowering URLLC-based edge networks model
		2.2.2 Transmission model
		2.2.3 DT empowered task offloading model
		2.2.4 Energy and power consumption model
		2.2.5 UAV deployment
		2.2.6 Problem formulation
	2.3 Proposed solutions
		2.3.1 Transmit power and computation resource optimisation
		2.3.2 Task offloading optimisation
		2.3.3 Proposed algorithm
	2.4 Numerical simulations
		2.4.1 Simulations setup
		2.4.2 Results and discussion
	2.5 Conclusion
	Appendix A: Approximations of
	and
	in
	References
3 AI-enabled data management for digital twin networks
	3.1 Introduction
		3.1.1 Importance of data management in DTNs
		3.1.2 Explanation of AI’s role in data management for DTNs
		3.1.3 Challenges in data management
		3.1.4 Three states of DTD
	3.2 The twinning process: AI-driven data acquisition, preprocessing, modeling, and data storage for DTNs
		3.2.1 Understanding the data acquisition
		3.2.2 Techniques for collecting data from various sources in DTNs
		3.2.3 Data preprocessing methods for cleaning, filtering, and transforming raw data
		3.2.4 Digital twin ontology and data modeling
		3.2.5 Storage architectures for managing large-scale data in DTNs
	3.3 AI-enabled data analysis and interpretation
		3.3.1 Overview of AI algorithms for data analytics in DTNs
	3.4 Ethical considerations and security aspects
		3.4.1 Ethical considerations of using AI in data management
		3.4.2 Security aspects, including data privacy and protection
		3.4.3 Privacy-preserving techniques for protecting sensitive data in DTNs
	3.5 Conclusion and future directions
		3.5.1 Summary of the chapter
		3.5.2 Remaining challenges and open research questions in AI-enabled data management for DTNs
	References
4 AI-based traffic analysis in digital twin networks
	4.1 DTNs ecosystem
	4.2 DTNs development efforts: literature review
		4.2.1 Networks in general
		4.2.2 Cellular networks: 5G and beyond
		4.2.3 Wireless networks
		4.2.4 Optical networks
		4.2.5 Satellite and aeronautic networks
		4.2.6 Vehicular networks
		4.2.7 Industrial IoT networks
	4.3 Key tasks in DTNs analysis
		4.3.1 AI-based network performance enhancement
		4.3.2 AI-based network management
		4.3.3 AI-based communication enhancement
		4.3.4 AI-based prediction analysis
		4.3.5 AI-based fault and anomaly detection
		4.3.6 AI-based security and privacy preservation
	4.4 Main AI models and tools harnessed by DTNs
		4.4.1 ML tools and models
		4.4.2 DL models and techniques
		4.4.3 RL and optimization techniques
		4.4.4 FL and collaborative learning
		4.4.5 Graph and network analysis techniques
	4.5 Main challenges in AI-based DTNs
		4.5.1 Key challenges
		4.5.2 Responsible AI considerations
	4.6 Conclusion and key points
	References
5 Digital twin empowered Open RAN of 6G networks
	5.1 Introduction
		5.1.1 Motivation and contribution
	5.2 Background on O-RAN
		5.2.1 RAN functionalities, building blocks, and disaggregation
		5.2.2 Toward the concept of Open RAN
		5.2.3 Definition of O-RAN architecture
		5.2.4 O-RAN as an enabler for 6G deployment
	5.3 The concept of digital twin
		5.3.1 Definition of digital twin
		5.3.2 General architecture of a DT system
		5.3.3 DT as an accelerator toward digitalization
	5.4 DT on O-RAN architecture: use cases
		5.4.1 Channel modeling for RAN optimization
		5.4.2 Network traffic forecasting and mobility management
		5.4.3 Security and threat detection
		5.4.4 Network fault detection
	5.5 Challenges and future directions
		5.5.1 Real-time synchronization
		5.5.2 Data flow security and privacy
		5.5.3 Data annotation
		5.5.4 Compliance
	5.6 Conclusions
	Acknowledgments
	References
6 Potentials of the digital twin in 6G communication systems
	6.1 Introduction
	6.2 Optimized planning, service testing, and rapid development of the 6G network
	6.3 Simplifying and accelerating the site deployment configuration
	6.4 Testing the impact of configuration and function changes
	6.5 Building platforms to train AI models for the 6G system
	6.6 Tackling the security and resiliency issues in 6G
	6.7 Efficient network slice management and orchestration
	6.8 Enabling 6G RAN optimization and effective traffic management
	6.9 Optimizing the 6G radio resource management
	6.10 Terahertz wave analysis in support of reconfigurable intelligent surfaces for enhanced 6G performance
	6.11 Enhancing the operation of mobile edge clouds in 6G
	6.12 Enabling 6G-based IIoT and industrial 6G use cases
	6.13 Conclusion
	Acknowledgments
	References
7 Digital twins for optical networks
	7.1 Introduction
	7.2 Current issues in optical networks
		7.2.1 Issue of suboptimal network operation
		7.2.2 Issue of limited automation
	7.3 DT development for optical networks
		7.3.1 Data collection layer
		7.3.2 Data fusion layer
		7.3.3 Modeling layer
	(1) Component-level models:
	(2) Integration with end-to-end models:
		7.3.4 Simulation and virtualization
		7.3.5 Application layer
		7.3.6 Recent work and case studies on optical DTs
	7.4 Open testbeds, open emulation tools, and open data
		7.4.1 Large-scale testbeds
		7.4.2 Open source emulation tools
		7.4.3 Open software and open data
	7.5 Conclusions
	Appendix A Acronyms
	References
8 Dynamic decomposition of service function chain using a deep reinforcement learning approach
	8.1 Introduction
		8.1.1 NFV as we know
		8.1.2 Microservices decomposed NFVs
		8.1.3 Digital twin
	8.2 Literature review
	8.3 Problem statement
		8.3.1 Objective
		8.3.2 Constraints
	8.4 Deep RL solution for microservice decomposition
		8.4.1 Reinforcement learning
		8.4.2 Environment
		8.4.3 State space
		8.4.4 Action space
		8.4.5 Reward function
		8.4.6 Decomposition Identifier
		8.4.7 Granularity criteria
		8.4.8 Re-architecture of VNF-FG
		8.4.9 Overview of the proposed model
	8.5 DNN architecture
	8.6 Simulation results
		8.6.1 Heuristic model
		8.6.2 Time complexity
		8.6.3 Netrail Topology
		8.6.4 BtEurope topology
		8.6.5 Nodal capacity
	8.7 Conclusions
	References
9 An Optimization-as-a-Service platform for 6G exploiting network digital twins
	9.1 OaaS platform: architectural overview
		9.1.1 Functional architecture
		9.1.2 OaaS system APIs
		9.1.3 A workflow example
	9.2 OaaS platform: network model
		9.2.1 Overview of standardized network models
		9.2.2 A transport and computing infrastructure model
	9.3 OaaS platform: use cases
		9.3.1 Dimensioning problems
		General problem statement
		Solving frameworks and approaches
		General problem statement
		Solving frameworks and approaches
		9.3.2 Operational problems
		General problem statement
		Solving framework and approaches
		General problem statement
		Solving frameworks and approaches
	Acknowledgments
	References
10 Robotics digital twin for 6G
	10.1 Introduction
	10.2 The Shift from Industry 4.0 to Industry 5.0
	10.3 Digital Twin as the Pillar of Industry 5.0
	10.4 ICT technologies and adaptation for Industry 5.0
	10.5 Unified role of Industrial E2E digital twin systems in Industry 5.0
	10.6 Fundamentals and challenges of digital twins for robotic systems
	10.7 Digital twins in real industrial environments
	10.8 From digital twins in Industry 4.0 to Industrial E2E digital twin systems in Industry 5.0
	10.9 The infrastructure behind industrial digital twins
		10.9.1 Computing and storage
		10.9.2 Connectivity
	10.10 Enablers for industrial digital twins
		10.10.1 Cloud-to-robot continuum for digital twins
		10.10.2 Computation offloading for digital twin
		10.10.3 Digital twin as a service
		10.10.4 Robot operating system framework
		10.10.5 Resource and service federation
	10.11 Open challenges to achieve Industrial E2E digital twin systems
	10.12 6G enablers and their applicability to E2E digital twins systems
	10.13 Context awareness
	10.14 Joint communication and sensing
	10.15 Semantic orchestration
	10.16 Distributed ledger technology federation
	10.17 Artificial intelligence
	10.18 Industrial E2E digital twin systems in collaborative robotic applications
	10.19 Manufacturing: localization and material inspection
	10.20 Warehouse: material handling and logistics
	10.21 Construction: safety takeover
	10.22 Healthcare: patient rehabilitation
	10.23 Conclusions
	Acknowledgments
	References
Index
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