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 single 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 presented 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 the 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：高通量卫星凭借其多波束传输技术，在性能上超越了传统卫星。低地球轨道巨型星座的兴起将高通量卫星的数据速率需求提升至每秒太比特量级，同时要求保持可接受的延迟水平。数据速率的激增需要配置多个调制解调器，其需求常超出单设备处理能力。因此，卫星采用多处理器架构，形成复杂的包交换网络。这可能导致潜在的内部拥塞，并给满足严格的服务质量约束带来挑战。尽管在星座级路由方面已有大量研究，但针对单颗高通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。本文提出了一种用于高通量卫星的在线最优流量分配与调度方法。该问题被表述为具有不同优先级数据流的多商品流实例。我们首先提出了一个完整时间范围的基准模型。采用模型预测控制方法，基于当前信息及预测时间范围内的流量预测实现自适应路由，同时允许预测存在偏差。重要的是，模型预测控制本质上适用于处理输入流量的不确定性。我们的方法通过优化和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，最小化数据包丢失。本方法的核心是一个专注于最优优先级调度的路由模型，旨在提升数据速率并保障服务质量。我们对模型的各个阶段进行了严格评估，并通过数值模拟将结果与传统方法进行比较。仿真结果表明，我们的方法性能接近后验最优解，展现了其在应对卫星通信挑战方面的效率与适应性。