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 digital twins
• Data sources, fidelity levels, and the management of 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 between digital and physical layers

Simulation & Co-simulation Architectures

• Standalone simulation versus co-simulation using real network telemetry
• Utilization of Ns-3, Unity, and emulation toolchains for integrated testing
• Strategies for scalability in 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
• Closed-loop control workflows and patterns for policy deployment

Real-Time Telemetry, Inference, and Feedback Loops

• Streaming telemetry architectures and the placement of low-latency inference
• Trade-offs between edge and cloud inference and model partitioning
• Design of safe 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 strategies for automated remediation

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 tracking, 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

• Construction of 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 data generated by the twin
• Implementation of a closed-loop test: telemetry → model inference → policy change in simulation

Summary and Next Steps