基于Swin-Transformer的短波协议信号识别

doi:10.11959/j.issn.1000-436x.2022209

通信学报 ›› 2022, Vol. 43 ›› Issue (11): 127-135.doi: 10.11959/j.issn.1000-436x.2022209

基于Swin-Transformer的短波协议信号识别

朱政宇¹^,²^,³, 陈鹏飞¹, 王梓晅¹, 巩克现¹, 吴迪⁴, 王忠勇¹

¹ 郑州大学电气与信息工程学院，河南郑州 450001
² 郑州大学河南省智能网络和数据分析国际联合实验室，河南郑州 450001
³ 郑州大学电子材料与系统国际联合研究中心，河南郑州 450001
⁴ 信息工程大学数据与目标工程学院，河南郑州 450001

修回日期:2022-10-19 出版日期:2022-11-25 发布日期:2022-11-01
作者简介:朱政宇（1988− ），男，河南周口人，博士，郑州大学副教授、硕士生导师，主要研究方向为无线通信与信号处理、智能反射表面技术、物理层安全技术等
陈鹏飞（1998− ），男，河南南阳人，郑州大学硕士生，主要研究方向为短波通信、智能信号处理
王梓晅（1998− ），男，河南周口人，郑州大学博士生，主要研究方向为通信信号处理、多源信息融合等
巩克现（1976− ），男，山东泰安人，博士，郑州大学教授、博士生导师，主要研究方向为无线通信信号分析与处理、信道编码、无线接入、目标监测及电子对抗等
吴迪（1984− ），男，福建建阳人，博士，信息工程大学讲师，主要研究方向为通信信号分析与智能处理、电子对抗等
王忠勇（1965− ），男，江西遂川人，博士，郑州大学教授、博士生导师，主要研究方向为通信信号处理等
基金资助:
国家重点研发计划基金资助项目(2019QY0302);中国博士后科学基金资助项目(2020M682345);河南省高校科技创新人才支持计划资助项目(23HASTIT019);河南省博士后经费资助项目(202001015)

Short wave protocol signals recognition based on Swin-Transformer

Zhengyu ZHU¹^,²^,³, Pengfei CHEN¹, Zixuan WANG¹, Kexian GONG¹, Di WU⁴, Zhongyong WANG¹

¹ School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
² Joint International Laboratory of Intelligent Network and Data Analysis in Henan Province, Zhengzhou University, Zhengzhou 450001, China
³ National Center for International Joint Research of Electronic Materials and Systems, Zhengzhou University, Zhengzhou 450001, China
⁴ College of Data Target Engineering, Information Engineering University, Zhengzhou 450001, China

Revised:2022-10-19 Online:2022-11-25 Published:2022-11-01
Supported by:
The National Key Research and Development Program of China(2019QY0302);China Postdoctoral Science Foundation Funded Project(2020M682345);Program for Science and Technology Innovation Talents in Universities of Henan Province(23HASTIT019);Henan Postdoctoral Foundation Program(202001015)

摘要/Abstract

摘要：

针对短波复杂信道环境下信号所属协议识别困难的问题，提出一种基于Swin-Transformer神经网络模型的短波协议信号识别算法。首先使用时频分析方法得到信号的灰度时频图作为神经网络的输入；其次设计一种基于Swin-Transformer的神经网络模型，对信号时频图进行特征提取；最后将特征与协议建立映射关系，从而实现信号协议的识别。仿真实验结果表明，在信噪比大于 -4 dB的高斯信道下，所提算法的识别准确率接近100%，高于现有算法。此外，在强干扰以及多径时延衰落的信道条件下，所提算法仍具有较高的短波协议信号识别率。

关键词: 短波协议信号识别, 神经网络, 时频分析, 多径时延衰落, Swin-Transformer

Abstract:

Aiming at the problem that it is difficult to identify the protocol to which the signal belongs in the complex SW channel environment, a SW protocol signal recognition algorithm based on Swin-Transformer neural network model was proposed.Firstly, the gray-scale time-frequency map of the signal was obtained by using the time-frequency analysis method as the input of the neural network.Secondly, a neural network model based on swing transformer was designed to extract the features of the signal time-frequency map.Finally, the mapping relationship between the features and the protocol was established to realize the recognition of the signal protocol.The simulation results show that the recognition accuracy of the proposed algorithm is close to 100% in the Gaussian channel with SNR greater than -4 dB, which is higher than the existing algorithms.In addition, under the channel conditions of strong interference and multipath delay fading, the proposed algorithm still has a high SW protocol signals recognition rate.

Key words: SW protocol signals recognition, neural network, time frequency analysis, multipath delay fading, Swin-Transformer

中图分类号:

TN92

朱政宇, 陈鹏飞, 王梓晅, 巩克现, 吴迪, 王忠勇. 基于Swin-Transformer的短波协议信号识别[J]. 通信学报, 2022, 43(11): 127-135.

Zhengyu ZHU, Pengfei CHEN, Zixuan WANG, Kexian GONG, Di WU, Zhongyong WANG. Short wave protocol signals recognition based on Swin-Transformer[J]. Journal on Communications, 2022, 43(11): 127-135.

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脉冲序号	频率/Hz
0	625
1	1 625
2	875
3	1 875
4	1 125
5	2 125
6	1 375
7	2 375

硬件与软件	配置信息
GPU	NVIDIA GeForce RTX 2080Ti
CPU	Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50 GHz
内存	64 GB
运行系统	Ubuntu 18.04 64 bit
Python	3.8
CUDA	11.0
PyTorch	1.7.1

基于Swin-Transformer的短波协议信号识别

Short wave protocol signals recognition based on Swin-Transformer

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参考文献 28

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