Paper Info
Title
Expectation-Maximization For Scheduling Problems In Satellite Communication
Abstract
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.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9413088
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
DocType
ISSN
Citations 
Conference
1051-4651
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Werner Bailer132847.96
martin winter232.21
Johannes Ebert300.34
Joel Flavio400.34
Karin Plimon502.03