6G • Core Network
GNN Dynamic Routing Engine
Graph Neural Network Topology-Aware Packet Dispatcher for 6G Mesh
AI routing system modeling the complex, rapidly shifting 3D space-air-ground network topology as an interactive neural graph to optimize paths.
Technical Explanation
In 6G Space-Air-Ground-Ocean Integrated Networks (SAGIN), satellite orbits, high-altitude drones, and terrestrial mesh nodes create a highly dynamic non-Euclidean topology. Traditional OSPF or BGP routing algorithms take seconds to converge, causing massive packet drops. The GNN Dynamic Routing Engine models moving nodes, laser cross-links, and queue depths as a graph neural network, computing optimal multi-hop paths in milliseconds before orbital link disconnections occur.
Key Functions
- Models highly dynamic 3D space-air-ground topologies as topological neural graphs
- Predicts link breaks and route congestion 5-10 seconds before they occur
- Computes optimal multi-hop packet routing paths across satellites and ground towers
- Converges in sub-milliseconds compared to minutes for legacy routing protocols
Specifications
ITU-T Y.3172 (ML for Networks), IEEE JSAC Machine Learning in Network Graphs
Interfaces
SAGIN Topology IngestionCore Routing EngineInter-Satellite Laser Router
Related 6G Concepts
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