  With the application of the fifth-generation wireless communication
technologies, more smart terminals are being used and generating huge amounts
of data, which has prompted extensive research on how to handle and utilize
these wireless data. Researchers currently focus on the research on the
upper-layer application data or studying the intelligent transmission methods
concerning a specific problem based on a large amount of data generated by the
Monte Carlo simulations. This article aims to understand the endogenous
relationship of wireless data by constructing a knowledge graph according to
the wireless communication protocols, and domain expert knowledge and further
investigating the wireless endogenous intelligence. We firstly construct a
knowledge graph of the endogenous factors of wireless core network data
collected via a 5G/B5G testing network. Then, a novel model based on graph
convolutional neural networks is designed to learn the representation of the
graph, which is used to classify graph nodes and simulate the relation
prediction. The proposed model realizes the automatic nodes classification and
network anomaly cause tracing. It is also applied to the public datasets in an
unsupervised manner. Finally, the results show that the classification accuracy
of the proposed model is better than the existing unsupervised graph neural
network models, such as VGAE and ARVGE.
