Expectation-Maximization For Scheduling Problems In Satellite Communication
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)(2020)
摘要
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.
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关键词
scheduling problems,satellite communication,unsupervised machine learning,Ka-band frequency plan optimization,expectation-maximization framework,active antenna array satellite configuration,input configuration
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