基于改进深度残差网络算法的智能干扰识别

马键; 张广晋; 张磊; 戴经纬

doi:10.11959/j.issn.1000-0801.2022233

您当前的位置：

首页 >

文章列表页 >

基于改进深度残差网络算法的智能干扰识别

研究与开发 | 更新时间：2024-06-05

- 基于改进深度残差网络算法的智能干扰识别
- Improved ResNet algorithm based intelligent interference identification
- 电信科学 2022年38卷第10期页码：98-106
- 作者机构：
- 作者简介：
  
  [ "马键（1984- ），男，中国移动通信有限公司研究院无线与终端技术研究所技术经理，主要研究方向为无线网络智能优化、无线网管功能及产品开发工作" ]
  [ "张广晋（1990- ），男，中国移动通信有限公司研究院无线与终端技术研究所工程师，主要研究方向为无线网络智能优化、方案设计、AI算法研究及系统开发" ]
  [ "张磊（1984- ），男，中国移动通信有限公司研究院无线与终端技术研究所工程师，主要研究方向为产品解决方案设计" ]
  [ "戴经纬（1985- ），女，中国移动通信有限公司研究院无线与终端技术研究所工程师，主要研究方向包括无线网络智能化应用需求分析、方案设计、系统开发" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022233
  中图分类号： TN929.5
- 网络出版日期：2022-10，
  
  纸质出版日期：2022-10-20
- 稿件说明：
移动端阅览
马键, 张广晋, 张磊, 等. 基于改进深度残差网络算法的智能干扰识别[J]. 电信科学, 2022,38(10):98-106.

Jian MA, Guangjin ZHANG, Lei ZHANG, et al. Improved ResNet algorithm based intelligent interference identification[J]. Telecommunications science, 2022, 38(10): 98-106.
马键, 张广晋, 张磊, 等. 基于改进深度残差网络算法的智能干扰识别[J]. 电信科学, 2022,38(10):98-106. DOI： 10.11959/j.issn.1000-0801.2022233.

Jian MA, Guangjin ZHANG, Lei ZHANG, et al. Improved ResNet algorithm based intelligent interference identification[J]. Telecommunications science, 2022, 38(10): 98-106. DOI： 10.11959/j.issn.1000-0801.2022233.

摘要

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。

Abstract

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.

关键词

Keywords

references

段晓东 , 孙滔 . 面向融合、智慧、低碳的5G技术演进 [J ] . 电信科学 , 2022 , 38 ( 3 ): 3 - 9 .

DUAN X D , SUN T . Towards converged,intelligent,low-carbon characterized 5G-Advanced technologies [J ] . Tele-communications Science , 2022 , 38 ( 3 ): 3 - 9 .

DAHLMAN E , PARKVALL S , SKOLD J . 5G NR:the next generation wireless access technology [M ] . New York : Academic , 2020 .

宋心刚 , 张冬晨 , 李行政 , 等 . 2.6 GHz频段5G上行干扰分析与识别研究 [J ] . 电信工程技术与标准化 , 2021 , 34 ( 4 ): 74 - 81 .

SONG X G , ZHANG D C , LI X Z , et al . Research on interfer-ence analyzing and recognition of 5G in 2.6 GHz [J ] . Telecom Engineering Technics and Standardization , 2021 , 34 ( 4 ): 74 - 81 .

陈凯 . 5G 干扰特征识别及解决方案研究 [J ] . 邮电设计技术 , 2021 ( 4 ): 34 - 39 .

CHEN K . Research on 5G network interference characteristics recognition and solution [J ] . Designing Techniques of Posts and Telecommunications , 2021 ( 4 ): 34 - 39 .

DEB S , MONOGIOUDIS P . Learning based uplink interference management in 4G LTE cellular systems [J ] . IEEE/ACM Transactions on Networking , 2015 , 23 ( 2 ): 398 - 411 .

DARWEESH T A , ELSAYED K . LTE uplink interference-aware scheduling using high interference and overload indicators [C ] // Proceedings of IFIP Wireless ＆ Mobile Networking Conference . Piscataway:IEEE Press , 2016 .

宋心刚 , 张冬晨 , 李行政 , 等 . 5G NR 上行干扰问题研究 [J ] . 电信工程技术与标准化 , 2020 , 33 ( 3 ): 9 .

SONG X G , ZHANG D C , LI X Z , et al . Research on uplink in-terference of 5G NR [J ] . Telecom Engineering Technics and Standardization , 2020 , 33 ( 3 ): 9 .

韩文冬 . 5G 基站上行干扰分析工具设计与实现 [J ] . 现代信息科技 , 2021 , 5 ( 4 ): 53 - 55 , 59 .

HAN W D . Design and implementation of uplink interference analysis tool for 5G base station [J ] . Modern Information Tech-nology , 2021 , 5 ( 4 ): 53 - 55 , 59 .

李攀攀 , 谢正霞 , 乐光学 , 等 . 基于深度学习的无线通信接收方法研究进展与趋势 [J ] . 电信科学 , 2022 , 38 ( 2 ): 1 - 17 .

LI P P , XIE Z X , YUE G X , et al . Research progress and trends of deep learning based wireless communication receiving method [J ] . Telecommunications Science , 2022 , 38 ( 2 ): 1 - 17 .

SCHMIDHUBER J . Deep learning in neural networks:an overview [J ] . Neural Networks , 2015 ( 61 ): 85 - 117 .

LU L Q , YI Y H , HUANG F L , et al . Integrating local CNN and global CNN for script identification in natural scene images [J ] . IEEE Access , 2019 ( 7 ): 52669 - 52679 .

SCHMIDHUBER J . Deep learning in neural networks [J ] . Neural Netw , 2015 .

YANG W , JING Z , YANG C , et al . A deep CNN method for underwater image enhancement [C ] // Proceedings of 2017 IEEE International Conference on Image Processing (ICIP) . Piscataway:IEEE Press , 2017 .

DOLZ J , AYED I B , JING Y , et al . HyperDense-Net:a hyper-densely connected CNN for multi-modal image segmentation [J ] . IEEE Transactions on Medical Imaging , 2018 ( 99 ).

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

LI B Q , HE Y Y . An improved ResNet based on the adjustable shortcut connections [J ] . IEEE Access , 2018 ( 6 ): 18967 - 18974 .

ROY S K , MANNA S , SONG T C , et al . Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2021 , 59 ( 9 ): 7831 - 7843 .

YOO H , HAN S , CHUNG K . Diagnosis support model of cardiomegaly based on CNN using ResNet and explainable feature map [J ] . IEEE Access , 2021 ( 9 ): 55802 - 55813 .

WU Z , SHEN C , HENGEL A . Wider or deeper:revisiting the ResNet model for visual recognition [J ] . Pattern Recognition , 2016 ( 90 ): 119 - 133 .

JIAO J B , TU W C , HE S F , et al . FormResNet:formatted residual learning for image restoration [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2017 : 1034 - 1042 .

YUE Z , LUO J Q , HUSHENG F , et al . Study on the identification method of human upper limb flag movements based on inception-ResNet double stream network [J ] . IEEE Access , 2020 ( 9 ): 85764 - 85784 .

浏览量

365

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向垂直行业专网的确定性SLA指标分级研究

5G多路冗余传输技术综述

星地融合下的手机直连关键技术研究

配电网5G通信架构及其时延可靠性研究

基于大数据的5G流量驻留比极限值研究