基于大数据挖掘的移动通信网络故障诊断方法研究

Research on Fault Diagnosis Method of Mobile Communication Network Based on Big Data Mining

随着移动通信网络技术的飞速发展,网络将会使用多种技术互补的方式替代传统的单一存在形式,实现共同发展。随着网络逐渐密集化和异构化,如何诊断网络故障成为了一个难题。此外,经典的网络故障诊断技术需要基于海量数据集才能实现故障诊断工作,但在一般应用中,采用人工的方式标注和分类大量数据集将面临成本过高的问题。该背景下,基于生成对抗网络(Generative Adversarial Network,GAN),提出了一种新的异构无线网络故障诊断方法,并通过仿真实验,证明该方法可以显著提高网络故障诊断的准确率。

With the rapid development of mobile communication network technology,the network will use a variety of complementary technologies to replace the traditional single form of existence and achieve common development.With the gradual densification and heterogeneity of the network,it becomes a challenge to diagnose network faults.In addition,classical network fault diagnosis techniques need to be based on massive data sets to achieve fault diagnosis work,but in general applications,using a manual approach to label and classify large data sets will face the problem of high cost.In this context,a new fault diagnosis method for heterogeneous wireless networks is proposed based on Generative Adversarial Network(GAN),and it is demonstrated through simulation experiments that the method can significantly improve the accuracy of network fault diagnosis.