基于改进深度残差网络算法的智能干扰识别

现网运维人力成本高、效率低，如何快速精准识别网络干扰类型、提高运维人员工作效率，成为亟待解决的问题。提出一种基于改进深度残差网络（ResNet）的智能干扰识别方法，通过对接运营商北向网管的通信干扰数据接口，对干扰数据进行采集和预处理，并结合现网专家经验对历史干扰数据类型进行标注和校正，形成离线干扰数据集。再将干扰频域信息进行图像化生成干扰频谱波形图，并针对不同干扰类型进行图像处理和数据处理。之后根据业务特点对传统ResNet算法进行改进，通过提取单一干扰类型特征，确定各特征在复合干扰类型中的权重，达到对任意干扰类型识别的目的。最后通过导入已训练好的模型对干扰数据进行在线识别，有效提高干扰识别的准确率和效率。

Improved ResNet algorithm based intelligent interference identification

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.