UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification

Insup Lee, Wonjun Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalIEEE Communications Letters
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Automatic modulation classification
  • generative adversarial networks
  • IQ constellations
  • single model training

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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