Research on Wireless Network Optimization based on Attention Mechanism

Abstract

【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.

Keywords

• wireless;troubleshooting;accuracy;attention mechanism