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Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification
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Qinyan MA, Jing XIAO, Zeqi SHAO, Duona ZHANG, Yufeng WANG, Wenrui DING. Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification",
author="Qinyan MA, Jing XIAO, Zeqi SHAO, Duona ZHANG, Yufeng WANG, Wenrui DING",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400080"
}
%0 Journal Article
%T Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification
%A Qinyan MA
%A Jing XIAO
%A Zeqi SHAO
%A Duona ZHANG
%A Yufeng WANG
%A Wenrui DING
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400080
TY - JOUR
T1 - Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification
A1 - Qinyan MA
A1 - Jing XIAO
A1 - Zeqi SHAO
A1 - Duona ZHANG
A1 - Yufeng WANG
A1 - Wenrui DING
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400080
Abstract: automatic modulation classification (AMC) serves a challenging yet crucial role in wireless communication. Despite deep learning-based approaches being widely used in signal processing, they are challenged by signal distribution variations, especially in various channel conditions. In this paper, we introduce Frequency-learning adversarial networks (FLANs) based on transfer learning for cross-scenario signal classification. This method utilizes the stability in the frequency spectrum by introducing a frequency adaptation (FA) technique to incorporate target channel information into source domain signals. To address the unpredictable interference in the channel, a fitting channel adaptation (FCA) module is used to reduce the differences between the two domains caused by variations in the channel environment. Experimental results illustrate that FLANs outperforms state-of-the-art transfer approaches, demonstrating an improved top-1 classification accuracy by 5.2% in high signal-to-noise ratio (SNR) scenes on a cross-scenario real collected dataset (CSRC2023), which will be made publicly available soon.
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