Machine Learning for Satellite Communications Operations

This article introduces the application of machine learning (ML)-based procedures in real-world satellite communication operations. While the application of ML in image processing has led to unprecedented advantages in new services and products, the application of ML in wireless systems is still in its infancy. In particular, this article focuses on the introduction of ML-based mechanisms in satellite network operation centers such as interference detection, flexible payload configuration, and congestion prediction. Three different use cases are described, and the proposed ML models are introduced. All the models have been constructed using real data and considering current operations. As reported in the numerical results, the proposed ML-based techniques show good numerical performance: the interference detector presents a false detection probability decrease of 44 percent, the flexible payload optimizer reduces the unmet capacity by 32 percent, and the traffic predictor reduces the prediction error by 10 percent compared to other approaches. In light of these results, the proposed techniques are useful in the process of automating satellite communication systems.

Automated summary

This article introduces the application of machine learning in satellite communication network operations, including interference detection, flexible payload configuration, and congestion prediction. The proposed ML techniques show good performance in real operations, including reducing false detection probability, unmet capacity, and prediction error.