UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification

Insup Lee, Wonjun Lee

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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
Pages (from-to)355-358
Number of pages4
JournalIEEE Communications Letters
Volume26
Issue number2
DOIs
Publication statusPublished - 2022 Feb 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation (NRF) of Korea grant funded by the Korea Government (MSIT) under Grant 2019R1A2C2088812.

Publisher Copyright:
© 1997-2012 IEEE.

Keywords

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

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification'. Together they form a unique fingerprint.

Cite this