Representation Learning of Knowledge Graph for Wireless Communication Networks

• URL: http://arxiv.org/abs/2208.10496v1
• Date: Mon, 22 Aug 2022 07:36:34 GMT
• Title: Representation Learning of Knowledge Graph for Wireless Communication Networks
• Authors: Shiwen He, Yeyu Ou, Liangpeng Wang, Hang Zhan, Peng Ren, Yongming Huang
• Abstract summary: This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols. 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.
• Score: 21.123289598816847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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