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Gaussian Low-pass Channel Attention Convolution Network for RF Fingerprinting
  • +2
  • Shunjie Zhang,
  • Tianhao Wu,
  • Wei Wang,
  • Ronghui Zhan,
  • Jun Zhang
Shunjie Zhang
National University of Defense Technology College of Electronic Science and Technology

Corresponding Author:735204780@qq.com

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Tianhao Wu
National University of Defense Technology College of Electronic Science and Technology
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Wei Wang
National University of Defense Technology College of Electronic Science and Technology
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Ronghui Zhan
National University of Defense Technology College of Electronic Science and Technology
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Jun Zhang
National University of Defense Technology College of Electronic Science and Technology
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Abstract

Radio frequency (RF) fingerprinting is a challenging and important technique in individual identification of wireless devices. Recent work has used deep learning-based classifiers on ADS-B signal without missing aircraft ID information. However, traditional methods are difficult to obtain well performance accuracy for classical deep learning methods to recognize RF signals. This letter proposes a Gaussian Low-pass Channel Attention Convolution Network (GLCA-Net), where a Gaussian Low-pass Channel Attention module (GLCAM) is designed to extract fingerprint features with low frequency. Particularly, in GLCAM, we design a Frequency-Convolutional Global Average Pooling (F-ConvGAP) module to help channel attention mechanism learn channel weights in frequency domain. Experimental results on the datasets of large-scale real-world ADS-B signals show that our method can achieve an accuracy of 92.08%, which is 6.21% higher than Convolutional Neural Networks.
08 Sep 2022Submitted to Electronics Letters
08 Sep 2022Submission Checks Completed
08 Sep 2022Assigned to Editor
17 Sep 2022Reviewer(s) Assigned
16 Apr 2023Review(s) Completed, Editorial Evaluation Pending
28 Apr 2023Editorial Decision: Revise Major
28 May 20231st Revision Received
30 May 2023Submission Checks Completed
30 May 2023Assigned to Editor
30 May 2023Review(s) Completed, Editorial Evaluation Pending
02 Jun 2023Editorial Decision: Accept