The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestrial networks under rapidly time-varying topologies and intermittent gateway visibility. Leveraging the global control capabilities of a geostationary (GEO)-resident software-defined networking (SDN) controller, we introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations. This makes it a promising approach for achieving low-latency and robust data delivery in highly dynamic LEO networks. Specifically, we formulate a constrained stochastic optimization problem and employ a residual reinforcement learning framework to optimize opportunistic routing for reducing transmission delay. Simulation results over multiple days of orbital data demonstrate that our method achieves significant improvements in queue length reduction compared to classical backpressure and other well-known queueing algorithms.

翻译：大规模低地球轨道卫星星座的激增，迫切需要智能路由策略，以在快速时变的拓扑结构和间歇性网关可见性条件下，有效地将数据传送到地面网络。利用地球静止轨道软件定义网络控制器的全局控制能力，我们引入了机遇路由，其目标是通过将数据包转发至任何当前可用的地面网关而非固定目的地，以最小化传输时延。这使其成为在高度动态的低轨网络中实现低时延和鲁棒数据传输的一种有前景的方法。具体而言，我们构建了一个约束随机优化问题，并采用残差强化学习框架来优化机遇路由以降低传输时延。基于多日轨道数据的仿真结果表明，与经典背压算法及其他知名排队算法相比，我们的方法在队列长度减少方面取得了显著改进。