iSatCR：基于图的联合在轨计算与路由用于低轨数据传输
iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery
AI总结：
本文提出iSatCR，通过联合在轨计算与路由优化提升低轨卫星数据传输效率，采用图嵌入和深度强化学习处理复杂动态网络。
摘要：
将大量低轨卫星产生的地球观测数据回传地面处理消耗大量在轨带宽并加剧空地链路瓶颈。多数先前工作集中在优化星座内原始数据路由，但无法应对数据量激增。最近， onboard计算的进步使能现场处理数据，从而显著减少需传输的数据量。本文提出iSatCR，一种基于图的分布式方法，联合优化在轨计算与路由以提升传输效率。在iSatCR中，设计了新的图嵌入利用位移特征聚合和分布式消息传递捕捉卫星状态，然后提出基于图的分布式深度强化学习算法，在受限的在轨存储条件下推导联合计算-路由策略以处理LEO网络的复杂性和动态性。大量实验表明iSatCR优于基线方法，特别是在高负载情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
