JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

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Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification

Author(s): Qinyan MA, Jing XIAO, Zeqi SHAO, Duona ZHANG, Yufeng WANG, Wenrui DING
Affiliation(s): School of Electrical and Information Engineering, Beihang University, Beijing 100191, China; more
Corresponding email(s): maqinyan17373036@buaa.edu.cn, zhangduona@buaa.edu.cn
Key Words: Frequency spectrum; Generative adversarial network; Transfer learning; Automatic modulation classification; Wireless communication c Zhejiang University Press 2024

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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,in press.https://doi.org/10.1631/FITEE.2400080

@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",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400080"
}

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%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 Frontiers of Information Technology & Electronic Engineering
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%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2400080"

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T1 - Frequency-learning adversarial networks based on transfer learning for cross-scenarios signal modulation classification
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A1 - Jing XIAO
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A1 - Duona ZHANG
A1 - Yufeng WANG
A1 - Wenrui DING
J0 - Frontiers of Information Technology & Electronic Engineering
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EP -
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Y1 - in press
PB - Zhejiang University Press & Springer
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doi="https://doi.org/10.1631/FITEE.2400080"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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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