This paper 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 on its infancy. In particular, this paper focuses on the introduction 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 ML-based proposed techniques can improve a certain key performance indicator of each use case at least a 10%. In light of the results, the proposed techniques are useful in the process of automating satellite communication systems.
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