In the near future, very high throughput satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also dynamic. A solution to this problem is given by flexible payload architectures; however, they require that resource management is performed autonomously and with low latency. In this paper, we propose the use of supervised machine learning, in particular a classification algorithm using a neural network, to manage the resources available in flexible payload architectures. Use cases are presented to demonstrate the effectiveness of the proposed approach, and a discussion is made on all the challenges that are presented.

中文翻译：

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用于超高吞吐量卫星系统中功率和带宽管理的监督机器学习

在不久的将来，超高吞吐量卫星 (VHTS) 系统的交通需求预计会出现大幅增长。然而，这种增长在服务区域内不会是均匀的，而且也是动态的。这个问题的解决方案是灵活的有效载荷架构。但是，它们要求资源管理以低延迟自主执行。在本文中，我们建议使用监督机器学习，特别是使用神经网络的分类算法，来管理灵活有效载荷架构中可用的资源。提供用例以证明所提出方法的有效性，并对提出的所有挑战进行讨论。