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Duration 21 hours
Course Outline
Foundations: Digital Twins and 6G Convergence
- Application of digital twin concepts to telecommunications networks
- 6G service categories and requirements driving the adoption of twins
- Data sourcing, 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 across 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 scalability in large-scale twin scenarios
AI-Native Optimization Techniques
- Applying supervised and reinforcement learning for radio resource management
- Employing online learning, transfer learning, and domain adaptation for twin-to-field transfer
- Defining closed-loop control workflows and policy deployment patterns
Real-Time Telemetry, Inference, and Feedback Loops
- Architecting streaming telemetry and positioning low-latency inference
- Balancing edge versus cloud inference with model partitioning strategies
- Designing secure feedback loops and human-in-the-loop controls
Digital Twin Fidelity, Validation & Uncertainty Quantification
- Metrics for assessing twin accuracy and validation methodologies
- Methods for quantifying and mitigating model uncertainty
- Leveraging digital twins for SLA verification and performance assurance
Orchestration, Automation & Intent-Driven Operations
- Connecting twins with orchestration planes and intent-based APIs
- Implementing CI/CD and testing pipelines for twin models and ML artifacts
- Deploying 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, 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
- Practical examples of predictive maintenance and capacity planning
Hands-On Labs and Mini-Project
- Constructing 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
- Executing a closed-loop test: telemetry \u2192 model inference \u2192 policy change in simulation
Summary and Next Steps
Requirements
- Background in telecommunications networking, RAN, or core network engineering
- Proficiency with simulation tools or network emulation
- Practical knowledge of Python and fundamental machine learning principles
Target Audience
- Telecommunications engineers and network architects specializing in next-generation networks
- AI/ML engineers focused on network optimization and digital twin implementations
- Research engineers and simulation specialists investigating 6G use cases
