Low Earth Orbit (LEO) satellite networks introduce unique congestion control (CC) challenges due to frequent handovers, rapidly changing round-trip times (RTTs), and non-congestive loss. This paper presents the first comprehensive, emulation-driven evaluation of CC schemes in LEO networks, combining realistic orbital dynamics via the LeoEM framework with targeted Mininet micro-benchmarks. We evaluated representative CC algorithms from three classes, loss-based (Cubic, SaTCP), model-based (BBRv3), and learning-based (Vivace, Sage, Astraea), across diverse single-flow and multi-flow scenarios, including interactions with active queue management (AQM). Our findings reveal that: (1) handover-aware loss-based schemes can reclaim bandwidth but at the cost of increased latency; (2) BBRv3 sustains high throughput with modest delay penalties, yet reacts slowly to abrupt RTT changes; (3) RL-based schemes severely underperform under dynamic conditions, despite being notably resistant to non-congestive loss; (4) fairness degrades significantly with RTT asymmetry and multiple bottlenecks, especially in human-designed CC schemes; and (5) AQM at bottlenecks can restore fairness and boost efficiency. These results expose critical limitations in current CC schemes and provide insight for designing LEO-specific data transport protocols.

翻译：低地球轨道卫星网络因频繁切换、快速变化的往返时延和非拥塞丢包，给拥塞控制带来了独特挑战。本文首次通过仿真驱动的综合评估，结合LeoEM框架的真实轨道动力学与精准Mininet微基准测试，对低轨网络中的拥塞控制方案进行了研究。我们评估了三类代表性拥塞控制算法：基于丢包的（Cubic、SaTCP）、基于模型的（BBRv3）和基于学习的（Vivace、Sage、Astraea），覆盖了单流和多流场景（包括与主动队列管理的交互）。研究发现：(1) 切换感知的丢包算法能恢复带宽，但以增加时延为代价；(2) BBRv3在中等时延惩罚下维持高吞吐量，但对突发RTT变化响应缓慢；(3) 基于强化学习的方案在动态条件下性能严重不足，尽管对非拥塞丢包具有显著抗性；(4) RTT不对称和多瓶颈会导致公平性显著下降，尤其在人为设计的拥塞控制方案中；(5) 瓶颈节点处的主动队列管理可恢复公平性并提升效率。这些结果揭示了现有拥塞控制方案的关键局限性，为设计面向低轨卫星的专用数据传输协议提供了见解。