AI and Digital Twins in 6G Networks Training Course

Foundations: Digital Twins and 6G Convergence

• Applying digital twin concepts to telecom networks
• 6G service classes and requirements that drive twin adoption
• Data sources, fidelity levels, and managing the twin lifecycle

Modelling 6G Components and Environments

• Representing RAN elements, fronthaul/midhaul/backhaul, and edge compute in twin models
• Considerations for channel, propagation, and THz/mmWave modelling
• Temporal granularity and synchronisation between digital and physical layers

Simulation & Co-simulation Architectures

• Comparing standalone simulation with co-simulation using real network telemetry
• Using Ns-3, Unity, and emulation toolchains for integrated testing
• Strategies for scaling large-scale twin scenarios

AI-Native Optimisation Techniques

• Supervised and reinforcement learning for radio resource management
• Online learning, transfer learning, and domain adaptation for transferring knowledge from twins to the field
• Closed-loop control workflows and policy deployment patterns

Real-Time Telemetry, Inference, and Feedback Loops

• Streaming telemetry architectures and low-latency inference placement
• Trade-offs between edge and cloud inference, and model partitioning
• Designing safe feedback loops and human-in-the-loop controls

Digital Twin Fidelity, Validation & Uncertainty Quantification

• Metrics for twin accuracy and validation methodologies
• Techniques for quantifying and mitigating model uncertainty
• Leveraging digital twins for SLA verification and performance assurance

Orchestration, Automation & Intent-Driven Operations

• Integrating twins with orchestration planes and intent-based APIs
• CI/CD and testing pipelines for twin models and ML artifacts
• Policy engines and automated remediation strategies

Security, Privacy & Trust in Twin-Enabled Networks

• Data governance, privacy-preserving modelling, and federated twin approaches
• Threat models for twin synchronisation and model integrity
• Auditing, provenance, and explainability for AI-driven decisions

Case Studies and Domain Applications

• Industrial automation and networked digital twins for manufacturing
• Validation of mobility, autonomous systems, and XR services
• Operational examples of predictive maintenance and capacity planning

Hands-On Labs and Mini-Project

• Building a small-scale digital twin of a RAN segment using ns-3 and a visualisation engine
• Training a lightweight ML model for anomaly detection using twin-generated data
• Implementing a closed-loop test: telemetry \u2192 model inference \u2192 policy change in simulation

Summary and Next Steps