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.

中文翻译：

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卫星通信运营的机器学习

本文介绍了基于机器学习（ML）的过程在实际卫星通信操作中的应用。虽然ML在图像处理中的应用已在新服务和新产品中带来了空前的优势，但ML在无线系统中的应用仍处于起步阶段。特别是，本文重点介绍在卫星网络运营中心引入基于ML的机制，例如干扰检测，灵活的有效负载配置和拥塞预测。描述了三种不同的用例，并介绍了所提出的ML模型。所有模型都是使用实际数据并考虑当前操作而构建的。正如数值结果中所报告的那样，所提出的基于ML的技术显示出良好的数值性能：与其他方法相比，干扰检测器的错误检测概率降低了44％，灵活的有效载荷优化器将未满足的容量降低了32％，流量预测器将预测误差降低了10％。根据这些结果，所提出的技术在使卫星通信系统自动化的过程中很有用。