Short wave protocol signals recognition based on Swin-Transformer

被引：0
|
作者：
Zhu Z. [1 ,2 ,3 ]
Chen P. [1 ]
Wang Z. [1 ]
Gong K. [1 ]
Wu D. [4 ]
Wang Z. [1 ]
机构：
[1] School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou
[2] Joint International Laboratory of Intelligent Network and Data Analysis in Henan Province, Zhengzhou University, Zhengzhou
[3] National Center for International Joint Research of Electronic Materials and Systems, Zhengzhou University, Zhengzhou
[4] College of Data Target Engineering, Information Engineering University, Zhengzhou
来源：
Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 11期
基金：
中国博士后科学基金; 国家重点研发计划;
关键词：
multipath delay fading; neural network; SW protocol signals recognition; Swin-Transformer; time frequency analysis;
D O I：
10.11959/j.issn.1000-436x.2022209
中图分类号：
学科分类号：
摘要：
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. © 2022 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：127 / 135
页数：8
相关论文
共 28 条
  • [11] LIN S G, ZENG Y, GONG Y., Learning of time-frequency attention mechanism for automatic modulation recognition, IEEE Wireless Communications Letters, 11, 4, pp. 707-711, (2022)
  • [12] HUANG Z H, LI S, YANG X H, Et al., OAE-EEKNN: an accurate and efficient automatic modulation recognition method for underwater acoustic signals, IEEE Signal Processing Letters, 29, pp. 518-522, (2022)
  • [13] WAN L, WANG Q, PAPIR A, Et al., Generalized end-to-end loss for speaker verification, Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4879-4883, (2018)
  • [14] XU Z J, SAVVARIS A, TSOURDOS A, Et al., High-frequency band automatic mode recognition using deep learning, Proceedings of 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), pp. 1-5, (2018)
  • [15] YANG S H, PENG H, XU M K, Et al., Ultra-short wave specific signal spectrogram recognition based on convolution neural network, Systems Engineering and Electronics, 41, 4, pp. 744-751, (2019)
  • [16] ZHA X, XU M K, PENG H, Et al., Specific protocol signal recognition based on deep residual network, Acta Electronica Sinica, 47, 7, pp. 1532-1537, (2019)
  • [17] ZHA X., Research on digital modulation signal detection, recognition and demodulation based on neural network, (2020)
  • [18] HE K M, ZHANG X Y, REN S Q, Et al., Deep residual learning for image recognition, Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016)
  • [19] LIU Z, LIN Y T, CAO Y, Et al., Swin transformer: hierarchical vision transformer using shifted windows, Proceedings of 2021 IEEE/ CVF International Conference on Computer Vision (ICCV), pp. 9992-10002, (2021)
  • [20] VASWANI A, SHAZEER N, PARMAR N, Et al., Attention is all you need, Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), pp. 5998-6008, (2017)
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