High Throughput Satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega constellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ several processors, forming a complex packet-switch network. This can lead to potential internal congestion and challenges in adhering to strict quality of service (QoS) constraints. While significant research exists on constellation-level routing, a literature gap remains on the internal routing within a singular HTS. The intricacy of this internal network architecture presents a significant challenge to achieve high data rates. This paper introduces an online optimal flow allocation and scheduling method for HTSs. The problem is treated as a multi-commodity flow instance with different priority data streams. An initial full time horizon model is proposed as a benchmark. We apply a model predictive control (MPC) approach to enable adaptive routing based on current information and the forecast within the prediction time horizon while allowing for deviation of the latter. Importantly, MPC is inherently suited to handle uncertainty in incoming flows. Our approach minimizes packet loss by optimally and adaptively managing the priority queue schedulers and flow exchanges between satellite processing modules. Central to our method is a routing model focusing on optimal priority scheduling to enhance data rates and maintain QoS. The model's stages are critically evaluated, and results are compared to traditional methods via numerical simulations. Through simulations, our method demonstrates performance nearly on par with the hindsight optimum, showcasing its efficiency and adaptability in addressing satellite communication challenges.

翻译：高吞吐量卫星凭借其多波束传输性能优于传统卫星。低地球轨道巨型星座的兴起将高吞吐量卫星的数据速率需求提升至太比特/秒量级，同时要求可接受的延迟。数据速率的激增要求配备多个调制解调器，这往往超出单个设备的处理能力。因此，卫星采用多个处理器构建复杂的分组交换网络，可能导致内部拥塞及严格服务质量约束的遵守难题。尽管星座级路由已有大量研究，但针对单颗高吞吐量卫星内部路由的文献仍存在空白。这种内部网络架构的复杂性对实现高数据速率构成了重大挑战。本文提出了一种适用于高吞吐量卫星的在线最优流量分配与调度方法。该问题被建模为包含不同优先级数据流的多商品流实例。首先提出全时段优化模型作为基准，继而采用模型预测控制方法实现基于当前信息与预测时域内预报的自适应路由，同时允许后续预测产生偏差。值得注意的是，模型预测控制天然适用于处理输入流的不确定性。该方法通过最优自适应管理优先级队列调度器及卫星处理模块间的流量交换，最大限度降低数据包丢失率。核心在于构建聚焦最优优先级调度的路由模型以提升数据速率并保障服务质量。通过数值仿真对各模型阶段进行严格评估，并与传统方法进行性能对比。仿真结果表明，所提方法性能几乎接近事后最优解，充分展现了其在应对卫星通信挑战中的高效性与自适应性。