用强化学习优化低轨卫星动态路由，降低数据传输延迟。
Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems
- 基于残差强化学习，动态选择可用地面网关转发数据
- 相比传统方法，队列长度平均减少42%以上
- 适合高动态低轨卫星网络的实时低时延通信场景
大规模低地球轨道（LEO）卫星星座的兴起，推动了在快速变化的拓扑结构和间歇性网关可见性条件下，实现高效数据向地面网络传输的智能路由策略需求。利用位于地球静止轨道（GEO）的软件定义网络（SDN）控制器的全局控制能力，我们提出一种机会式路由机制，通过将数据包转发至当前可用的任意地面网关，而非固定目的地，以最小化交付延迟。该方法在多个日周期的轨道数据仿真中表现优异，相较于经典背压算法及其他知名排队算法，在队列长度减少方面取得显著提升，验证了其在高度动态的LEO网络中实现低时延、鲁棒数据传输的潜力。
原文摘要 · Abstract (English)
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.
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