Fault detection and diagnosis method for heterogeneous wireless network based on GAN

Xiaorong ZHU; Peipei ZHANG

doi:10.11959/j.issn.1000-436x.2020165

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Fault detection and diagnosis method for heterogeneous wireless network based on GAN

Papers | 更新时间：2024-06-05

- Fault detection and diagnosis method for heterogeneous wireless network based on GAN
- Journal on Communications Vol. 41, Issue 8, Pages: 110-119(2020)
- 作者机构：
  
  南京邮电大学江苏省无线通信重点实验室，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61871237);Program to Cultivate Middle-aged and Young Science Leaders of Universities of Jiangsu Province and Key Research and Development Plan of Jiangsu Province(BE2019017)
- DOI：10.11959/j.issn.1000-436x.2020165
  CLC： TN915.81
- Online First：2020-08，
  
  Published：25 August 2020
- 稿件说明：
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Xiaorong ZHU, Peipei ZHANG. Fault detection and diagnosis method for heterogeneous wireless network based on GAN[J]. Journal on Communications, 2020, 41(8): 110-119.
DOI：

Xiaorong ZHU, Peipei ZHANG. Fault detection and diagnosis method for heterogeneous wireless network based on GAN[J]. Journal on Communications, 2020, 41(8): 110-119. DOI： 10.11959/j.issn.1000-436x.2020165.

摘要

针对异构无线网络故障检测与诊断过程中，如何基于小数据量样本进行准确的故障检测与诊断模型的训练的问题，提出了基于生成对抗网络的异构无线网络故障检测与诊断算法。首先，分析了异构无线网络环境下的常见网络故障来源，通过 GAN 算法，在小数据量的网络故障样本的基础上，得到大量可靠数据集。然后，基于这些数据，利用极端梯度提升算法选择故障检测阶段输入参数的最优特征组合，并完成故障检测与诊断。仿真结果表明，所提算法可以实现对异构无线网络更加准确而高效的故障检测与诊断，准确率可达98.18%。

Abstract

Aiming at the problem that in the process of network fault detection and diagnosis

how to train the precise fault diagnosis and detection model based on small data volume

a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly

the common network fault sources in heterogeneous wireless network environment was analyzed

and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then

the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks

with an accuracy of 98.18%.

关键词

Keywords

references

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