UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification

Insup Lee, Wonjun Lee

    Research output: Contribution to journalArticlepeer-review

    20 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

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