RESEARCH PAPER
Improved Graph Convolutional Neural Networks-based Cellular Network Fault Diagnosis
1
School of Computer Science & Engineering, Linyi University, Linyi, 276000, China
Submission date: 2024-08-06
Final revision date: 2024-09-18
Acceptance date: 2024-10-15
Online publication date: 2024-10-19
Publication date: 2024-10-19
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194672
HIGHLIGHTS
- This study uses extremal gradient enhancement to select the optimal feature subset.
- This study uses graph convolutional neural network to extract the fault depth feature.
- This technology utilizes Naive Bayes models for pre-diagnosis
KEYWORDS
TOPICS
ABSTRACT
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. The method designed in this study uses extremal gradient enhancement to select the optimal feature subset, and uses graph convolutional neural network to extract the fault depth feature. At the same time, the knowledge data fusion technology is raised to expand the training data of the fault diagnosis model. This technology utilizes Naive Bayes models for pre-diagnosis and enhances graph convolutional neural networks to control the influence of pre-diagnosis outcomes and training dataset size on model training accuracy. In the experiment, the fault diagnosis accuracy and efficiency of the raised method are better than those of the traditional network fault diagnosis methods. This algorithm can diagnose faults in complex cellular network environment, which has high accuracy and practicability, and can effectively improve user experience.
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CITATIONS (1):
1.
Capsule networks for intelligent fault diagnosis: a roadmap of recent advancements and challenges
Yang Dalian, Zou Junjun, Long Hui
Expert Systems with Applications
Yang Dalian, Zou Junjun, Long Hui
Expert Systems with Applications
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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