基于Swin-Transformer的短波协议信号识别

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

Abstract

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

CLC Number:

TN92

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.

Figures/Tables 15

References 28

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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

Short wave protocol signals recognition based on Swin-Transformer

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