基于深度学习的无线电信号分类

兰州理工大学学报 ›› 2021, Vol. 47 ›› Issue (4): 106-110.

• 自动化技术与计算机技术 • 上一篇下一篇

基于深度学习的无线电信号分类

刘金霞^*

甘肃省无线电监测站, 甘肃兰州 730000

收稿日期:2021-04-27 出版日期:2021-08-01 发布日期:2021-09-07
通讯作者: 刘金霞(1972-),女,甘肃省会宁人,硕士,高级工程师.Email:green2013@dingtalk.com

Radio signal classification based on deep learning

LIU Jin-xia

Gansu Radio Monitoring Station, Lanzhou 730000, China

Received:2021-04-27 Online:2021-08-01 Published:2021-09-07

摘要/Abstract

摘要： 对无线电信号分类的相关技术进行了研究,提出一种新的基于残差神经网络和群卷积神经网络的深度学习网络来实现无线电的分类.该神经网络基于同相分量信号和正交分量信号组成的样本进行训练,实验结果显示,在10 dB时对24种信号的分类准确率达到了95.69%,揭示了该网络架构的有效性与实用性.

关键词: 深度学习, 残差神经网络, 群卷积神经网络, 无线电信号分类

Abstract: The related technologies of radio signal classification are first studied, and then a novel deep learning network based on residual neural network and group convolutional neural network is proposed to realize radio signal classification. The neural network is trained based on sample composed of in-phase component signal and quadrature component signal. The experimental results show that the classification accuracy of 24 kinds of signals reaches 95.69% at 10 dB, and the effectiveness and practicability of the network architecture are revealed.

Key words: deep learning, residual neural network, group convolutional neural network, radio signal classification

中图分类号:

TP315

刘金霞. 基于深度学习的无线电信号分类[J]. 兰州理工大学学报, 2021, 47(4): 106-110.

LIU Jin-xia. Radio signal classification based on deep learning[J]. Journal of Lanzhou University of Technology, 2021, 47(4): 106-110.

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基于深度学习的无线电信号分类

Radio signal classification based on deep learning

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