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cloud.google.com·6h ago
AI Research
arXiv CS.AI·24d ago·5 min read
AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks
TL;DR
An AI-driven framework using Graph Neural Networks (GNNs) optimizes real-time multi-hop relay selection in NR-V2X networks, achieving up to 11.3% connectivity gain and 100x faster execution than traditional methods.
✦ Why It Matters
Implement AI-driven relay optimization in your NR-V2X systems to enhance connectivity and reduce latency.
Key Takeaways
- Explore AI frameworks for real-time network optimization.
- Utilize GNNs for efficient relay selection in urban environments.
- Consider MILP for initial optimal relay decisions.
- Implement findings to improve smart city connectivity.
How It Works
The framework models the vehicular network as a graph, where nodes represent vehicles and RSUs, and edges capture radio-link characteristics. It uses a GINE to learn from optimal relay decisions generated by an offline MILP model, enabling real-time relay selection that adapts to changing urban conditions.