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.

翻译：低地球轨道（LEO）卫星互联网服务的兴起导致需求不断增长，常常超过可用数据速率并影响服务质量。虽然部署更多卫星提供了短期解决方案，但设计具有增强传输能力的高性能卫星则提供了更可持续的途径。实现所需的高容量需要在卫星有效载荷内部互连多个调制解调器组。然而，在极高吞吐量卫星内部数据包路由方面的研究存在显著空白。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，用于实时最优流量分配与优先级队列调度。我们将该问题建模为多商品流实例，并采用在线内点法迭代求解路由与调度优化问题。该方法能够最小化丢包率，并以较低的计算开销支持实时重路由。我们在下一代极高吞吐量卫星模型上进行了仿真测试，结果表明，相较于参考的批量优化方法及传统方法，本方法具有显著优势。