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.

翻译：低轨卫星网络因其快速变化的拓扑结构、链路波动和队列动态特性，对多跳语义传输构成重大挑战，导致端到端性能联合依赖于路由选择、中继处理及语义载荷自适应调整。现有研究通常独立优化路由或语义传输，难以适应局部观测下动态卫星场景。针对该问题，本文提出GraphJSCR——一种基于图的联合路由与语义编码方法，用于动态低轨卫星网络中的多跳语义传输。将卫星星座建模为时变有向图，并将转发过程形式化为部分可观测序贯决策问题。设计图表示学习模块以编码局部拓扑、链路状态、队列条件、数据包上下文及语义传输状态。基于所学表示，所提出的决策网络联合确定下一跳选择、中继处理层级和语义传输预算，以平衡端到端语义质量与传输延迟。语义编解码器借鉴SwinJSCC框架开发。仿真结果表明，与基准方法相比，GraphJSCR在语义保真度与传输效率之间实现更快收敛与更优权衡。