Joint Semantic Coding and Routing for Multi-Hop Semantic Transmission in LEO Satellite Networks

2026-04-14

AI Summary

This work addresses the challenge of end-to-end semantic transmission in low Earth orbit (LEO) satellite networks, where rapidly changing topology, unstable links, and dynamic queue states hinder performance when routing or semantic encoding is optimized in isolation. The authors model the satellite constellation as a time-varying directed graph and formulate multi-hop forwarding as a partially observable sequential decision-making problem. They propose GraphJSCR, a framework that leverages graph neural networks to jointly optimize next-hop selection, relay processing level, and semantic encoding budget by integrating local topology, link status, queue information, packet context, and semantic transmission state. To the best of our knowledge, this is the first approach to achieve end-to-end co-optimization of routing, relaying, and semantic transmission in dynamic LEO satellite networks. Experiments demonstrate that GraphJSCR converges faster than baseline methods and achieves a superior trade-off between semantic fidelity and transmission efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Low Earth Orbit satellite networks pose significant challenges to multi-hop semantic transmission because rapidly changing topology, link variability, and queue dynamics make end-to-end performance jointly depend on routing, relay processing, and semantic payload adaptation. Existing studies usually optimize routing or semantic transmission separately and are therefore not well suited to dynamic satellite scenarios under local observations. To address this issue, this paper proposes GraphJSCR, a graph-based joint routing and semantic coding method for multi-hop semantic transmission in dynamic Low Earth Orbit satellite networks. The satellite constellation is modeled as a time-varying directed graph, and the forwarding process is formulated as a partially observable sequential decision problem. A graph representation learning module is designed to encode local topology, link status, queue conditions, packet context, and semantic transmission states. Based on the learned representation, the proposed decision network jointly determines next-hop selection, relay processing level, and semantic transmission budget to balance end-to-end semantic quality and transmission delay. The semantic encoder-decoder is developed with reference to the SwinJSCC framework. Simulation results demonstrate that GraphJSCR achieves faster convergence and a better tradeoff between semantic fidelity and transmission efficiency than benchmark methods.

Problem

• LEO satellite networks
• multi-hop semantic transmission
• dynamic topology
• joint optimization
• semantic communication

Innovation

• semantic communication
• LEO satellite networks
• graph neural networks
• joint optimization
• multi-hop routing