The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi- signal modulation classification problem. In this paper, we developed a method for co-channel multi- signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Co-Channel Multi-Signal Modulation Classification Based on Convolution Neural Network


    Beteiligte:
    Yin, Zhendong (Autor:in) / Zhang, Rui (Autor:in) / Wu, Zhilu (Autor:in) / Zhang, Xiaojun (Autor:in)


    Erscheinungsdatum :

    01.04.2019


    Format / Umfang :

    1381677 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Classification of Brain Tumor based on Convolution Neural Network

    Srivastava, Mayank / Mishra, Adarsh / Dixit, Pratibha | IEEE | 2024


    VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

    Janak TRIVEDI / Mandalapu Sarada DEVI / Dave DHARA | DOAJ | 2021

    Freier Zugriff