Sending massive Earth observation data produced by low Earth orbit (LEO) satellites back to the ground for processing consumes a large amount of on-orbit bandwidth and exacerbates the space-to-ground link bottleneck. Most prior work has concentrated on optimizing the routing of raw data within the constellation, yet cannot cope with the surge in data volume. Recently, advances in onboard computing have made it possible to process data in situ, thus significantly reducing the data volume to be transmitted. In this paper, we present iSatCR, a distributed graph-based approach that jointly optimizes onboard computing and routing to boost transmission efficiency. Within iSatCR, we design a novel graph embedding utilizing shifted feature aggregation and distributed message passing to capture satellite states, and then propose a distributed graph-based deep reinforcement learning algorithm that derives joint computing-routing strategies under constrained on-board storage to handle the complexity and dynamics of LEO networks. Extensive experiments show iSatCR outperforms baselines, particularly under high load.

翻译：低地球轨道卫星产生的大量对地观测数据需回传地面处理，这不仅消耗大量在轨带宽，更加剧了星地链路的瓶颈问题。现有研究多聚焦于优化星座内原始数据的路由策略，但难以应对数据量的激增。近期星载计算技术的进步使得原位数据处理成为可能，可显著缩减待传输数据量。本文提出基于分布式图方法的iSatCR框架，通过联合优化星载计算与路由来提升传输效率。在该框架中，我们设计了一种采用移位特征聚合与分布式消息传递的新型图嵌入方法以捕获卫星状态，进而提出基于分布式图深度强化学习的算法，在星载存储受限条件下推导联合计算-路由策略，以应对低轨网络的复杂性与动态性。大量实验表明，iSatCR在各类场景中均优于基线方案，尤其在高负载条件下表现突出。