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arXiv 2409.01488cs.NIeess.SP
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面向高吞吐量卫星的在线凸优化 onboard 路由

Online Convex Optimization for On-Board Routing in High-Throughput Satellites

Polytechnique Montréal(蒙特利尔高等学院)

Olivier Bélanger, Jean-Luc Lupien, Olfa Ben Yahia, Stéphane Martel, Antoine Lesage-Landry, Gunes Karabulut Kurt

AI总结:

本文提出基于在线凸优化的模型预测控制方法,用于高吞吐量卫星的实时数据包路由优化,有效减少数据包丢失并支持低开销的实时重路由。

摘要:

低轨道卫星互联网服务的兴起导致了日益增长的需求,往往超过可用的数据速率,从而影响服务质量。尽管增加更多卫星可以提供短期解决方案,但设计具有更高性能的卫星并增强传输能力则提供了更可持续的解决方案。实现必要的高容量需要在卫星有效载荷内连接多个调制解调器银行。然而,针对极高吞吐量卫星内部数据包路由的研究存在明显空白。为此,我们提出了一种基于在线凸优化的模型预测控制方法,用于实时最优流分配和优先级队列调度。我们将问题建模为一个多商品流实例,并采用在线内部点方法迭代求解路由和调度优化。该方法最小化数据包丢失并支持低计算开销的实时重路由。我们的方法在下一代极高吞吐量卫星模型上进行了仿真测试,证明了其有效性,与参考批量优化方法和传统方法相比具有优势。

英文摘要:

The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and compromising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.

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。

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