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级别，同时要求可接受的延迟。这种数据速率的激增需要多个调制解调器，往往超出单一设备的处理能力。因此，卫星采用多个处理器形成复杂的分组交换网络，可能导致内部拥塞，并难以满足严格的服务质量约束。尽管星座级路由研究已较为充分，但单个高通量卫星内部路由的研究仍存在空白。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通量卫星的在线最优流分配与调度方法。该问题被建模为具有不同优先级数据流的多商品流实例，并首先提出一个全时间域基准模型。我们采用模型预测控制方法，基于当前信息与预测时间域内的流量预测实现自适应路由，同时允许后者存在偏差。值得注意的是，模型预测控制本质上能够处理输入流的随机性。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交换，最小化数据包丢失。其核心在于建立以最优优先级调度为关键的路由模型，以提升数据速率并维持服务质量。我们对模型各阶段进行了严格评估，并通过数值仿真与传统方法进行对比。仿真结果表明，该方法性能几乎接近事后最优解，充分证明了其在解决卫星通信挑战中的高效性与适应性。