AI and Digital Twins in 6G Networks Training Course

Foundations: The Convergence of Digital Twins and 6G

• Application of digital twin concepts within telecommunications networks.
• 6G service categories and requirements driving the adoption of twins.
• Management of data sources, fidelity levels, and the twin lifecycle.

Modeling 6G Components and Environments

• Representation of RAN elements, fronthaul/midhaul/backhaul, and edge compute within twin models.
• Considerations for channel, propagation, and THz/mmWave modeling.
• Temporal granularity and synchronization mechanisms between digital and physical layers.

Simulation & Co-simulation Architectures

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

AI-Native Optimization Techniques

• Application of supervised and reinforcement learning for radio resource management.
• Online learning, transfer learning, and domain adaptation for transitioning from twin to field deployment.
• Workflows for closed-loop control and patterns for policy deployment.

Real-Time Telemetry, Inference, and Feedback Loops

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

Digital Twin Fidelity, Validation & Uncertainty Quantification

• Metrics for assessing 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 modeling, and federated twin approaches.
• Threat models addressing twin synchronization and model integrity.
• Auditing, provenance, and explainability for AI-driven decision-making.

Case Studies and Domain Applications

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

Hands-On Labs and Mini-Project

• Constructing a small-scale digital twin of a RAN segment using ns-3 and visualization engines.
• Training lightweight ML models for anomaly detection using twin-generated data.
• Implementing closed-loop tests: telemetry → model inference → policy adjustment within simulation.

Summary and Future Directions