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

In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation...

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Bibliographic Details
Published in:arXiv.org 2019-06
Main Authors: Garau Luis, Juan Jose, Guerster, Markus, Inigo del Portillo, Crawley, Edward, Cameron, Bruce
Format: Article
Language:English
Subjects:
Algorithms
Broadband
Computer simulation
Demand
Machine learning
Optimization
Power consumption
Power management
Resource allocation
Satellites
Online Access:Get full text
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Summary:In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. 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 and inefficient. Therefore, it is desirable to automate the operation of these highly flexible satellites. This paper presents a novel power allocation approach based on Deep Reinforcement Learning (DRL) that represents the problem as continuous state and action spaces. We make use of the Proximal Policy Optimization (PPO) algorithm to optimize the allocation policy for minimum Unmet System Demand (USD) and power consumption. The performance of the algorithm is analyzed through simulations of a multibeam satellite system, which show promising results for DRL to be used as a dynamic resource allocation algorithm.
ISSN:2331-8422
DOI:10.48550/arxiv.1906.00571