AI and Digital Twins in 6G Networks Training Course

Foundations: Convergence of Digital Twins and 6G

• Application of digital twin concepts to telecom networks
• 6G service classes and requirements driving the adoption of twins
• Data sources, fidelity levels, and 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 mechanisms between digital and physical layers

Simulation and Co-simulation Architectures

• Comparison of standalone simulation versus co-simulation with live network telemetry
• Utilization of Ns-3, Unity, and emulation toolchains for integrated testing
• Scalability strategies for 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 twin-to-field migration
• Closed-loop control workflows and policy deployment patterns

Real-Time Telemetry, Inference, and Feedback Loops

• Streaming telemetry architectures and placement of low-latency inference
• Trade-offs between edge and cloud inference, along with model partitioning
• Design of secure feedback loops and human-in-the-loop control mechanisms

Digital Twin Fidelity, Validation, and Uncertainty Quantification

• Metrics for twin accuracy and corresponding validation methodologies
• Techniques for quantifying and mitigating model uncertainty
• Leveraging digital twins for SLA verification and performance assurance

Orchestration, Automation, and 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, and Trust in Twin-Enabled Networks

• Data governance, privacy-preserving modeling, and federated twin approaches
• Threat models regarding 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

• Construction of 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
• Implementation of a closed-loop test: telemetry -> model inference -> policy change in simulation

Summary and Next Steps