AI and Digital Twins in 6G Networks Training Course

Foundations: Digital Twins and 6G Convergence

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

Modeling 6G Components and Environments

• Representing 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 & Co-simulation Architectures

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

AI-Native Optimization Techniques

• Using supervised and reinforcement learning for radio resource management
• Employing online learning, transfer learning, and domain adaptation for twin-to-field deployment
• Designing closed-loop control workflows and policy deployment patterns

Real-Time Telemetry, Inference, and Feedback Loops

• Architecting streaming telemetry and positioning low-latency inference
• Evaluating edge versus cloud inference trade-offs and model partitioning
• Creating safe feedback loops and implementing human-in-the-loop controls

Digital Twin Fidelity, Validation & Uncertainty Quantification

• Metrics for assessing twin accuracy and validation methodologies
• Methods for quantifying and reducing model uncertainty
• Leveraging digital twins for SLA verification and performance assurance

Orchestration, Automation & Intent-Driven Operations

• Connecting twins to orchestration planes and intent-based APIs
• Establishing CI/CD and testing pipelines for twin models and ML artifacts
• Implementing policy engines and automated remediation strategies

Security, Privacy & Trust in Twin-Enabled Networks

• Data governance, privacy-preserving modeling, and federated twin approaches
• Threat modeling for 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 for mobility, autonomous systems, and XR services
• Operational insights into predictive maintenance and capacity planning

Hands-On Labs and Mini-Project

• Constructing a small-scale RAN segment digital twin 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 → model inference → policy change in simulation

Summary and Next Steps