基于注意力机制的无线网络优化研究

Research on Wireless Network Optimization based on Attention Mechanism

【目的】无线网络设备故障诊断的意义在于帮助解决网络连接问题,提高用户体验,确保网络的稳定性和可靠性,然而现有方法的准确率有待进一步提高。【方法】针对该问题,文章在卷积神经网络(CNN)-门控循环单元(GRU)网络结构中引入了注意力机制,提出了一种基于CNN-GRU-Attention网络结构的无线网络设备故障诊断方法。【结果】相较于其他方法,文章所提方法在仿真实验中模型的准确率从91%提升到了98%,提升效果明显。【结论】文章所提方法提高了无线网络设备运营的质量和效率,提升了网络容量、功能以及可靠性,满足了人们日益增长的无线网络通信需求。

【Objective】The significance of wireless network equipment troubleshooting can be used to solve the network connection problems,improving user experience,and maintaining network stability and reliability.However,the accuracy of existing methods needs to be further improved.【Methods】To address this problem,this paper quotes the attention mechanism in the Convolutional Neural Networks(CNN)-Gated Recurrent Unit(GRU)network structure and proposes a wireless network equipment fault diagnosis method based on the CNN-GRU-Attention network structure.【Results】Compared with other methods in the simulation experiment,the method in this paper improved the accuracy of the model from 91%to 98%.【Conclusion】Through the research of this article,the quality and efficiency of wireless network equipment operation have been improved.The network capacity,functionality and reliability have also been improved,and people's growing communication needs for wireless networks have been met.