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Programa do Curso
Foundations: Digital Twins and 6G Convergence
- Concepts of digital twins applied to telecom networks
- 6G service classes and requirements that motivate 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
- Channel, propagation, and THz/mmWave modeling considerations
- Temporal granularity and synchronization between digital and physical layers
Simulation & Co-simulation Architectures
- Standalone simulation vs 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
- Edge vs cloud inference trade-offs and 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
Requisitos
- Experience in telecom networking, RAN or core network engineering
- Familiarity with simulation tools or network emulation
- Working knowledge of Python and basic machine learning concepts
Audience
- Telecom engineers and network architects focused on next-gen networks
- AI/ML engineers working on network optimization and digital twin applications
- Research engineers and simulation specialists exploring 6G use cases
21 Horas
