Automatic Modulation Recognition Method for Multiple Antenna System Based on Convolutional Neural Network

Juan Wang, Yu Wang, Wenmei Li, Guan Gui, Haris Gacanin, Fumiyuki Adachi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).

    Original languageEnglish
    Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728194844
    DOIs
    Publication statusPublished - 2020 Nov
    Event92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
    Duration: 2020 Nov 18 → …

    Publication series

    NameIEEE Vehicular Technology Conference
    Volume2020-November
    ISSN (Print)1550-2252

    Conference

    Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
    Country/TerritoryCanada
    CityVirtual, Victoria
    Period20/11/18 → …

    Keywords

    • Convolutional neural network
    • cooperative decision
    • deep learning
    • multiple antenna system
    • signal recognition

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering
    • Applied Mathematics

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