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On-line Access: 2024-08-27

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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

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author="Qinyan MA, Jing XIAO, Zeqi SHAO, Duona ZHANG, Yufeng WANG, Wenrui DING",
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doi="https://doi.org/10.1631/FITEE.2400080"
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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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