Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning

Tobias Rohe, Michael Kölle, Jan Matheis, Rüdiger Höpfl, Leo Sünkel, Claudia Linnhoff-Popien

2025

Abstract

Satellite communication is a key technology in our modern connected world. With increasingly complex hardware, one challenge is to efficiently configure links (connections) on a satellite transponder. Planning an optimal link configuration is extremely complex and depends on many parameters and metrics. The optimal use of the limited resources, bandwidth and power of the transponder is crucial. Such an optimization problem can be approximated using metaheuristic methods such as simulated annealing, but recent research results also show that reinforcement learning can achieve comparable or even better performance in optimization methods. However, there have not yet been any studies on link configuration on satellite transponders. In order to close this research gap, a transponder environment was developed as part of this work. For this environment, the performance of the reinforcement learning algorithm PPO was compared with the metaheuristic simulated annealing in two experiments. The results show that Simulated Annealing delivers better results for this static problem than the PPO algorithm, however, the research in turn also underlines the potential of reinforcement learning for optimization problems.

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Paper Citation


in Harvard Style

Rohe T., Kölle M., Matheis J., Höpfl R., Sünkel L. and Linnhoff-Popien C. (2025). Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 704-713. DOI: 10.5220/0013313000003890


in Bibtex Style

@conference{icaart25,
author={Tobias Rohe and Michael Kölle and Jan Matheis and Rüdiger Höpfl and Leo Sünkel and Claudia Linnhoff-Popien},
title={Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={704-713},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013313000003890},
isbn={978-989-758-737-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning
SN - 978-989-758-737-5
AU - Rohe T.
AU - Kölle M.
AU - Matheis J.
AU - Höpfl R.
AU - Sünkel L.
AU - Linnhoff-Popien C.
PY - 2025
SP - 704
EP - 713
DO - 10.5220/0013313000003890
PB - SciTePress