Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison
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garau20a.pdf
Description
Accepted version
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3.5 MB
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Author(s)
Luis, Juan Jose Garau
Pachler, Nils
Guerster, Markus
del Portillo, Inigo
Crawley, Edward
Cameron, Bruce
Date Issued
March 2020
Journal
IEEE Aerospace Conference Proceedings
Publisher
IEEE
Citation
Luis, Juan Jose Garau, Pachler, Nils, Guerster, Markus, del Portillo, Inigo, Crawley, Edward et al. 2020. "Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison." IEEE Aerospace Conference Proceedings.
Version
Author's final manuscript
Abstract
© 2020 IEEE. Automating resource management strategies is a key priority in the satellite communications industry. The future landscape of the market will be changed by a substantial increase of data demand and the introduction of highly flexible communications payloads able to operate and reconfigure hundreds or even thousands of beams in orbit. This increase in dimensionality and complexity puts the spotlight on Artificial Intelligence-based dynamic algorithms to optimally make resource allocation decisions, as opposed to previous fixed policies. Although multiple approaches have been proposed in the recent years, most of the analyses have been conducted under assumptions that do not entirely reflect operation scenarios. Furthermore, little work has been done in thoroughly comparing the performance of different algorithms. In this paper we compare some of the recently proposed dynamic resource allocation algorithms under realistic operational assumptions, addressing a specific problem in which power needs to be assigned to each beam in a multibeam High Throughput Satellite (HTS). We focus on Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Deep Reinforcement Learning, and hybrid approaches. Our multibeam operation scenario uses demand data provided by a satellite operator, a full radio-frequency chain model, and a set of hardware and time constraints present during the operation of a HTS. We compare these algorithms focusing on the following characteristics: time convergence, continuous operability, scalability, and robustness. We evaluate the performance of the algorithms against different test cases and make recommendations on the approaches that are likely to work better in each context.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/aero47225.2020.9172682
