Cursusaanbod

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

Vereisten

  • 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 Uren

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