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Course Outline
Foundations: Digital Twins and 6G Convergence
- Digital twin concepts applied to telecom networks.
- 6G service classes and requirements that drive the need for twin usage.
- Data sources, fidelity levels, and twin lifecycle management.
Modeling 6G Components and Environments
- Representing RAN elements, fronthaul/midhaul/backhaul, and edge compute in twin models.
- Considerations for channel, propagation, and THz/mmWave modeling.
- Temporal granularity and synchronization between digital and physical layers.
Simulation & Co-simulation Architectures
- Differences between standalone simulation and co-simulation with real network telemetry.
- Ns-3, Unity, and emulation toolchains for integrated testing.
- Scalability strategies for large-scale twin scenarios.
AI-Native Optimization Techniques
- Supervised and reinforcement learning for radio resource management.
- 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 safe feedback loops and human-in-the-loop controls.
Digital Twin Fidelity, Validation & Uncertainty Quantification
- Metrics for twin accuracy and validation methodologies.
- Techniques for quantifying and mitigating model uncertainty.
- Using digital twins for SLA verification and performance assurance.
Orchestration, Automation & 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 & 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
- Building 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 → model inference → policy change in simulation.
Summary and Next Steps
Requirements
- Experience in telecom networking, including RAN or core network engineering.
- Familiarity with simulation tools or network emulation.
- Working knowledge of Python and fundamental machine learning concepts.
Audience
- Telecom engineers and network architects focused on next-generation networks.
- AI/ML engineers working on network optimization and digital twin applications.
- Research engineers and simulation specialists exploring 6G use cases.
21 Hours
