Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning

TL;DR

This paper explores the application of reinforcement learning to optimize satellite transponder link configurations, comparing its performance with traditional metaheuristic methods, and highlights the potential of RL despite current limitations.

Contribution

It introduces a novel transponder environment for RL and compares PPO with simulated annealing for link configuration optimization in satellite communication.

Findings

Simulated annealing outperforms PPO in static link configuration tasks.

Reinforcement learning shows promising potential for future optimization problems.

The study provides a new environment for RL research in satellite systems.

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.