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.
Learning transferable visual models from natural language supervi- sion
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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.