This is not the latest version of this item. The latest version can be found here.
Deep Reinforcement Learning for Continuous Power Allocation in Flexible High Throughput Satellites
Name
garau19a.pdf
Description
Accepted version
Size
363.8 KB
Format
Adobe PDF
Checksum (MD5)
2d68bbd38605bd7a1c67e710b4fafe19
Author(s)
Luis, Juan Jose Garau
Guerster, Markus
del Portillo, Inigo
Crawley, Edward
Cameron, Bruce
Date Issued
June 2019
Journal
2019 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2019
Publisher
IEEE
Citation
Luis, Juan Jose Garau, Guerster, Markus, del Portillo, Inigo, Crawley, Edward and Cameron, Bruce. 2019. "Deep Reinforcement Learning for Continuous Power Allocation in Flexible High Throughput Satellites." 2019 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2019.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, making manual resource allocation impractical. Therefore, it is desirable to automate the operation of these highly flexible satellites. This paper presents a novel approach based on Deep Reinforcement Learning to allocate power in multibeam satellite systems. The proposed architecture represents the problem as continuous state and action spaces. We make use of the Proximal Policy Optimization algorithm to optimize the allocation policy for minimum unmet system demand and power consumption. Finally, the performance of the algorithm is analyzed through simulations of a multibeam satellite system. The analysis shows promising results for Deep Reinforcement Learning to be used as a dynamic resource allocation algorithm.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ccaaw.2019.8904901
