Karin Plimon
Paper download is intended for registered attendees only, and is
subjected to the IEEE Copyright Policy. Any other use is strongly forbidden.
Papers from this author
Expectation-Maximization for Scheduling Problems in Satellite Communication
Werner Bailer, Martin Winter, Johannes Ebert, Joel Flavio, Karin Plimon
Auto-TLDR; Unsupervised Machine Learning for Satellite Communication Using Expectation-Maximization
Abstract Slides Poster Similar
In this paper we address unsupervised machine learning for two use cases in satellite communication, which are scheduling problems: (i) Ka-band frequency plan optimization and (ii) dynamic configuration of an active antenna array satellite. We apply approaches based on the Expectation-Maximization (EM) framework to both of them. We compare against baselines of currently deployed solutions, and show that they can be significantly outperformed by the EM-based approach. In addition, the approaches can be applied incrementally, thus supporting fast adaptation to small changes in the input configuration.