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UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification

Authors
Lee, InsupLee, Wonjun
Issue Date
2월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Signal to noise ratio; Generative adversarial networks; Modulation; Training; Data models; Training data; Constellation diagram; Automatic modulation classification; generative adversarial networks; single model training; IQ~constellations
Citation
IEEE COMMUNICATIONS LETTERS, v.26, no.2, pp.355 - 358
Indexed
SCIE
SCOPUS
Journal Title
IEEE COMMUNICATIONS LETTERS
Volume
26
Number
2
Start Page
355
End Page
358
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138921
DOI
10.1109/LCOMM.2021.3131476
ISSN
1089-7798
Abstract
Deep learning has been widely applied to automatic modulation classification (AMC), and there have been many studies on data augmentation techniques using deep generative models to improve performance. However, existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. This letter presents UniQGAN, Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introduces multi-conditions embedding and multi-domains classification to leverage both conditions, i.e., modulation type and SNR. Experimental results show that UniQGAN effectively improves the AMC performance, while the training time is reduced.
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