Machine Learning for Satellite Communications Operations

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