图卷积神经网络在5G网络故障诊断中的研究

Research on Graph Convolutional Neural Networks for Fault Diagnosis in 5G Networks

5G技术的引入进一步增加了网络的复杂性,因此,运营商需要制定新策略来确保网络质量,以保持其竞争力。然而,随着网络规模的扩大,蜂窝网络的运维工作变得更加困难。传统的网络故障诊断方法过于依赖人力和专业知识,导致诊断效率低下且结果不尽如人意。因此,迫切需要采用智能高效的网络故障诊断方法来解决这些挑战。文章提出基于图卷积神经网络的蜂窝网络故障诊断方法,希望能够对5G网络故障诊断研究有所助益。

The introduction of 5G technology further increases the complexity of the network,therefore,operators need to develop new strategies to ensure network quality and maintain competitiveness.However,with the expansion of network scale,the operation and maintenance of cellular networks have become more difficult.Traditional network fault diagnosis methods rely too much on manpower and professional knowledge,resulting in low diagnostic efficiency and unsatisfactory results.Therefore,there is an urgent need to adopt intelligent and efficient network fault diagnosis methods to address these challenges.The article proposes a cellular network fault diagnosis method based on graph convolutional neural networks,hoping to be helpful for the research of 5G network fault diagnosis.