Improved ResNet algorithm based intelligent interference identification - Jian MA; Guangjin ZHANG; Lei ZHANG; Jingwei DAI (Dianxin kexue)
The labor cost of the current network operation and maintenance is high and the efficiency is low.How to quickly and accurately identify the type of network interference, and improve the work efficiency of the maintenance personnel has become an urgent problem to be solved.An intelligent interference identification method of improved deep residual network (ResNet) was studied.The interference data was collected and preprocessedby connecting with the communication interference data interface of the operator's northbound network management.The type of itwaslabelled and corrected by the current network experts to form an offline interference data set.Then the interference frequency domain information was used to generate the interference spectrum waveform image, and performed image processing and data processing for different interference types.After that, the traditional Res Net algorithm was improved according to the business characteristics to extract the features of single interference type.The features were weighted in the compound interference type to identify any type of interference.Finally, the interference data was identified online by importing the trained model, which effectively improved the accuracy and efficiency of interference identification.