Research on RF front-end fault monitoring method based on deep learning

RF front-end is the key part of signal transmission and reception in satellite communication system, and its performance directly affects the quality and efficiency of communication. In order to ensure the normal operation of the RF front-end and explore the fault situation of the system, this paper proposes a RF front-end fault monitoring method based on deep learning. Through the seven different RF front-end equivalent analog hardware system (using different LNA) using vector network analyzer for data acquisition, data processing. The deep learning module uses MATLAB-based Feedforward Neural Network (FNN)and Recurrent Neural Network (RNN). The comparison between the predicted waveform and the measured waveform in the two neural network simulation diagrams shows the accuracy of the fault monitoring results. According to the specified fault level rules, determine whether the RF front-end system is working properly. If there is a fault, determine whether it is a first-level fault, a second-level fault or a third-level fault. After testing, the fault monitoring of the RF front-end system can be realized and the faults can be classified. This method is of great significance in improving the accuracy of fault detection, improving system reliability, promoting technological innovation and expanding application fields. With the continuous development of technology, RF front-end fault monitoring based on deep learning will play a more important role in the future.