AI and Digital Twins in 6G Networks Training Course

Foundations: Digital Twins and 6G Convergence for Government

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

Modeling 6G Components and Environments for Government

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

Simulation & Co-simulation Architectures for Government

• Standalone simulation versus co-simulation with real network telemetry
• Use of Ns-3, Unity, and other emulation toolchains for integrated testing
• Scalability strategies for large-scale digital twin scenarios

AI-Native Optimization Techniques for Government

• Application of supervised and reinforcement learning to radio resource management
• Online learning, transfer learning, and domain adaptation for transitioning from twin to field operations
• Closed-loop control workflows and patterns for policy deployment

Real-Time Telemetry, Inference, and Feedback Loops for Government

• Architectures for streaming telemetry and placement of low-latency inference
• Trade-offs between edge and cloud inference and model partitioning strategies
• Design considerations for safe feedback loops and human-in-the-loop controls

Digital Twin Fidelity, Validation & Uncertainty Quantification for Government

• Metrics and methodologies for validating digital twin accuracy
• Techniques for quantifying and mitigating model uncertainty
• Utilizing digital twins for SLA verification and performance assurance

Orchestration, Automation & Intent-Driven Operations for Government

• Integration of digital twins with orchestration planes and intent-based APIs
• CI/CD pipelines and testing frameworks for digital twin models and machine learning artifacts
• Policy engines and automated remediation strategies

Security, Privacy & Trust in Twin-Enabled Networks for Government

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

Case Studies and Domain Applications for Government

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

Hands-On Labs and Mini-Project for Government

• Building a small-scale digital twin of a RAN segment using ns-3 and a visualization engine
• Training a lightweight machine learning model for anomaly detection using data generated by the digital twin
• Implementing a closed-loop test: telemetry → model inference → policy change in simulation

Summary and Next Steps for Government