UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification
Inseop Lee, Wonjun Lee · IEEE Communications Letters · 2021
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 presentsUniQGAN, Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introducesmulti-conditions embeddingandmulti-domains classificationto 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.