Supervised Machine Learning for Power and Bandwidth Management in VHTS Systems

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, to manage the resources available in flexible payload architectures. A use case is presented to demonstrate the effectiveness of the proposed approach and a discussion is made on all the challenges that are presented.