GraphJSCR uses graph representation learning to jointly decide next-hop routing, relay processing level, and semantic transmission budget in dynamic LEO satellite networks, yielding better semantic fidelity and delay tradeoffs than separate optimization baselines in simulations.
Fully-distributed dynamic packet routing for LEO satellite networks: A GNN-enhanced multi- agent reinforcement learning approach,
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Joint Semantic Coding and Routing for Multi-Hop Semantic Transmission in LEO Satellite Networks
GraphJSCR uses graph representation learning to jointly decide next-hop routing, relay processing level, and semantic transmission budget in dynamic LEO satellite networks, yielding better semantic fidelity and delay tradeoffs than separate optimization baselines in simulations.