Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Yu, Xiang (Herausgeber:in) / Yuan, Ruichen (Autor:in) / Xie, Jian (Autor:in)


    Erscheinungsdatum :

    30.10.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    SplitAMC: Split Learning for Robust Automatic Modulation Classification

    Park, Jihoon / Oh, Seungeun / Kim, Seong-Lyun | IEEE | 2023


    Depth Analysis in Deep Learning-Based Automatic Modulation Classification

    Osman Kaya / Tansal Güçlüoğlu / Hacı İlhan | DOAJ | 2024

    Freier Zugriff

    Robust Real-Time Traffic Surveillance with Deep Learning

    Jessica Fernández / José M. Cañas / Vanessa Fernández et al. | DOAJ | 2021

    Freier Zugriff

    Real Time Traffic Sign Classification Using Deep Learning

    Gurupriya, M. / Veluru, Abhilash / Venkat, Hruday et al. | Springer Verlag | 2025

    Freier Zugriff