发表机构
Texas A&M University at Qatar; Hamad Bin Khalifa University; University of Cambridge; Professionals for Smart Technology (PST); Qatar University（卡塔尔德州农工大学; 哈马德·本·哈利法大学; 剑桥大学; 智能技术专业人士组织; 卡塔尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种联合几何与QoS感知的光学LEO卫星网络路由框架，通过闭式中断表达式和角度约束掩蔽深度Q网络（AC-MDQN），在降低动作空间复杂性的同时实现接近最优的延迟性能。
AI 中文摘要
光学星间链路（ISLs）正成为现代LEO星座的骨干，提供高容量和低延迟，但引入了严格的几何和物理层约束。因此，此类网络中的路由必须考虑时变拓扑、抖动引起的中断以及面内和面间光学链路的异构可靠性，这些方面是经典最短路径或现有基于学习的方案未能完全捕获的。本文为光学LEO网络开发了一个联合几何与QoS感知路由框架。我们在高斯光束传播和指向误差下推导了中断概率的闭式表达式，并为不同ISL类别获得了解析的最大可行链路距离。这些关系将光束发散从优化变量中移除，并将光学可行性直接嵌入路由层，导致一个延迟-可靠性-容量约束的路由公式，该公式被证明是NP难的。为了实现可扩展的决策，我们将快照路由建模为马尔可夫决策过程，并引入了一个角度约束掩蔽深度Q网络（AC-MDQN），该网络集成了光学可行性掩蔽、基于势能的延迟整形以及围绕源-目的地大圆路径的几何感知走廊过滤器。这种设计显著降低了有效动作空间的复杂性，同时保持了接近最优的路由选择。在类似Starlink的星座上的模拟表明，AC-MDQN实现的端到端延迟在约束最短路径解的1%到2%以内，在不同指向抖动下保持鲁棒性，并通过奖励设计支持可控的跳数-延迟权衡。结果证实，所提出的框架为大规模光学LEO网络提供了一种高效且物理一致的路由解决方案。
英文摘要
Optical inter satellite links ISLs are becoming the backbone of modern LEO constellations offering high capacity and low latency but introducing stringent geometric and physical layer constraints Routing in such networks must therefore account for time varying topology jitter induced outage and the heterogeneous reliability of intra and inter plane optical links aspects that classical shortest path or existing learning based schemes do not fully capture This paper develops a joint geometric and QoS aware routing framework for optical LEO networks We derive a closed form outage expression under Gaussian beam propagation with pointing errors and obtain analytical maximum feasible link ranges for different ISL classes These relations remove beam divergence from the optimization variables and embed optical feasibility directly into the routing layer leading to a latency reliability capacity constrained routing formulation that is proved to be NP hard To enable scalable decision making we cast snapshot routing as a Markov decision process and introduce an angle constrained masked deep Q network AC MDQN that integrates optical feasibility masks potential based latency shaping and a geometry aware corridor filter around the source destination great circle path This design significantly reduces the effective action space complexity while preserving near optimal routing choices Simulations on a Starlink like constellation demonstrate that AC MDQN achieves end to end latency within 1 to 2 percent of constrained shortest path solutions remains robust under varying pointing jitter and supports controllable hop latency trade offs through reward design The results confirm that the proposed framework provides an efficient and physically consistent routing solution for large scale optical LEO networks
