Deep Reinforcement Learning for Continuous Power Allocation in Flexible High Throughput Satellites

• Main Authors: Luis, Juan Jose Garau (Author), Guerster, Markus (Author), del Portillo, Inigo (Author), Crawley, Edward (Author), Cameron, Bruce Gregory (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
• Format: Article
• Language: English
• Published: IEEE, 2021-11-22T18:47:23Z.
• Subjects: Article
• Online Access: Get fulltext

 © 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.