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Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

Ebenezer Ackah Amuah¹, Mingxiao Wu¹, Xiaorong Zhu¹

¹Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces an improved 4G/5G network fault diagnosis method using Graph Convolutional Neural Networks (GCN). The algorithm effectively identifies cellular network faults even with limited labeled data, enhancing communication reliability.

Keywords:

4G/5G network fault diagnosis graph convolutional neural network heterogeneous network

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Area of Science:

Telecommunications Engineering
Artificial Intelligence
Network Management

Background:

Cellular network fault diagnosis is critical for uninterrupted communication services.
Existing methods may struggle with limited labeled data in complex heterogeneous wireless networks.
Accurate fault identification is essential for maintaining network performance and user experience.

Purpose of the Study:

To propose an improved fault diagnosis algorithm for 4G/5G cellular networks.
To enhance diagnostic accuracy using Graph Convolutional Neural Networks (GCN) with minimal labeled samples.
To address the challenge of fault identification in heterogeneous wireless environments.

Main Methods:

Analysis of common 4G/5G network failure types.
Construction of a graph structure using network parameters, with datasets as nodes and similarities as edges.
Application of GCN for feature extraction, node classification, and cell fault type prediction.

Main Results:

The proposed GCN-based algorithm demonstrates superior performance in network fault diagnosis.
Effective fault prediction was achieved even with a small number of labeled samples.
Experimental validation using real-world driving test data confirmed the method's efficacy.

Conclusions:

The developed GCN algorithm offers an effective solution for 4G/5G network fault diagnosis.
The method significantly improves diagnostic accuracy compared to traditional algorithms, especially with limited labeled data.
This approach contributes to more reliable and efficient cellular communication services.