Improved Graph Convolutional Neural Networks-based Cellular Network Fault Diagnosis

To solve the problem of upstream and downlink interference in cellular networks, a graph convolutional neural networks-based novel fault diagnosis method for semi-supervised cellular networks is proposed. In the research design method, the extreme gradient enhancement technique is first used to enhance the fault diagnosis feature data of cellular networks. Then, the graph convolutional neural network is used to train and learn the fault diagnosis feature dataset of cellular networks, achieving fault diagnosis prediction of cellular networks. In the process of training the cellular network fault diagnosis model, data augmentation techniques were used to enhance the training level of the model, while Bayesian networks were used for pre diagnosis to improve the diagnostic accuracy of the modified model. The experimental results show that the cellular network fault diagnosis model constructed in the study can achieve a classification accuracy of 90% for training samples during training and testing, while other models can only achieve a maximum of about 85%. The model constructed by the research can achieve a diagnostic accuracy of over 90% in the practical application of cellular network fault diagnosis, while taking only 6 seconds. This algorithm can diagnose faults in complex cellular network environment, which has high accuracy and practicability, and can effectively improve user experience.