AI and Digital Twins in 6G Networks Training Course

Foundations: The Convergence of Digital Twins and 6G

• Application of digital twin concepts to telecom networks.
• 6G service classes and requirements that necessitate the use of twins.
• Data sources, fidelity levels, and lifecycle management of twins.

Modeling 6G Components and Environments

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

Simulation and Co-simulation Architectures

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

AI-Native Optimization Techniques

• Applying supervised and reinforcement learning for radio resource management.
• Employing online learning, transfer learning, and domain adaptation for twin-to-field transfer.
• 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, along with model partitioning.
• Designing secure feedback loops and human-in-the-loop controls.

Digital Twin Fidelity, Validation, and 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, and 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, and Trust in Twin-Enabled Networks

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

Case Studies and Domain Applications

• Industrial automation and networked digital twins for manufacturing.
• Mobility, autonomous systems, and XR service validation.
• Operational examples of predictive maintenance and capacity planning.

Hands-On Labs and Mini-Project

• Developing 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.
• Implementing a closed-loop test: telemetry to model inference to policy change in simulation.

Summary and Next Steps