Telecommunications Science ›› 2023, Vol. 39 ›› Issue (12): 53-64.doi: 10.11959/j.issn.1000-0801.2023257

• Research and Development • Previous Articles    

Fault diagnosis method for 5G networks based on data and knowledge

Qingya PAN, Heng ZHANG, Wenjie LIU, Xiaorong ZHU   

  1. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Revised:2023-12-15 Online:2023-12-01 Published:2023-12-01
  • Supported by:
    The National Natural Science Foundation of China(92067101);The Key Research and Development Program of Jiangsu Province(BE2021013-3);Jiangsu Province Postgraduate Research and Practice Innovation Program(KYCX21_0733)

Abstract:

Aiming at the low accuracy and operation and maintenance efficiency of existing 5G network fault diagnosis, a 5G network fault diagnosis algorithm based on data and knowledge was proposed.Firstly, the graph convolutional network (GCN) model was used to train the 5G network fault data set, and the fault diagnosis model was obtained.Then, a 5G network fault knowledge graph (KG) was constructed through knowledge integration, knowledge extraction, knowledge fusion and representation of multi-source data sources.Finally, by using knowledge graph to analyze the output results of fault diagnosis model, fault report could be generated, and the interpretability of fault diagnosis model output could be improved.The proposed method provides a new accurate and efficient solution for 5G network fault diagnosis, and the experiment shows that the accuracy of the fault model reaches 95%.In addition, the 5G network fault knowledge map can provide support for operations and maintenance personnel to help them analyze the causes of failures.

Key words: 5G, GCN, KG, fault diagnosis, knowledge extraction

CLC Number: 

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