Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning
This addresses the challenge of efficiently configuring satellite communication links, which is crucial for resource-limited transponders, but it is an incremental study applying existing methods to a new domain.
The paper tackled the problem of optimizing link configuration for satellite transponders, comparing reinforcement learning (PPO) with simulated annealing, and found that simulated annealing performed better for this static problem, though reinforcement learning showed potential.
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.