AI and Digital Twins in 6G Networks Training Course

Foundations: The Convergence of Digital Twins and 6G

• Application of digital twin concepts to telecommunications networks.
• 6G service classes and requirements that drive the adoption of digital twins.
• Data sources, fidelity levels, and lifecycle management for twins.

Modeling 6G Components and Environments

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

Simulation & Co-simulation Architectures

• Comparison of standalone simulation and co-simulation with live network telemetry.
• Use of 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 to radio resource management.
• Online learning, transfer learning, and domain adaptation for twin-to-field migration.
• Workflows for closed-loop control and patterns for policy deployment.

Real-Time Telemetry, Inference, and Feedback Loops

• Architectures for streaming telemetry and placement of low-latency inference.
• Trade-offs between edge and cloud inference, including model partitioning.
• Design of secure feedback loops and 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

• Integration of 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 concerning twin synchronization and model integrity.
• Auditing, provenance tracking, and explainability for AI-driven decisions.

Case Studies and Domain Applications

• Industrial automation and networked digital twins in manufacturing.
• Validation of mobility, autonomous systems, and XR services.
• Operational 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 a visualization engine.
• Training a lightweight ML model for anomaly detection using twin-generated data.
• Executing a closed-loop test: telemetry input → model inference → policy adjustment within simulation.

Summary and Next Steps