Communication Satellite Signal Quality Prediction and Control Model Using Bayesian Optimization Algorithm

Traditional communication satellite signal quality prediction methods, such as those based on statistical models or classical machine learning algorithms, are often difficult to cope with the high complexity and uncertainty in satellite communication systems. This article aims to use the Bayesian Optimization (BO) algorithm based on satellite communication signal quality prediction and management model to provide reliable and stable signal transmission services for satellite communication. In this article, based on BO algorithm, a satellite communication signal quality prediction and control model is established, and verified by experiments. Starting with satellite communication signal monitoring, analysis and modeling, BO algorithm is used to predict signal quality, and signal quality control is realized in combination with signal quality control model to improve the reliability and stability of satellite communication. The experimental results show that after controlling 2550 satellite signal quality data, the proportion of signal quality data between 81–100 partitions under BO algorithm is 46.7%. Under decision tree, random forest and Support Vector Machine (SVM), the proportion of signal quality data between 81–100 partitions is 10.2%, 12.3% and 19.9%, which obviously shows that the control effect of BO algorithm is better. BO algorithm is used to predict satellite communication signals, which improves the effect of signal quality control. The research results of this article can provide technical support for real-time monitoring and control of satellite communication signals.