This paper proposes the blind interference power estimation via deep learning approach exploiting the visualized wireless signal information. Blind adaptive array (BAA) signal processing is the powerful solution to suppress various kinds of interference such as inter-cell interference (ICI) and intersystem interference (ISysI) for which receivers cannot obtain a priori information represented as channel state information (CSI). However, BAAs cannot always suppress interference due to its blind nature. Depending on signal-to-interference power ration (SIR), adequate BAA algorithms should be switched. In order to estimate SIR in a blind manner, we propose to apply a convolutional neural network (CNN) trained by IQ constellation images where contains the desired and interference signals. This paper presents its methodology and fundamental possibility.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Blind SIR Estimation by Convolutional Neural Network Using Visualized IQ Constellation


    Beteiligte:
    Maruta, Kazuki (Autor:in) / Kojima, Shun (Autor:in) / Ahn, Chang-Jun (Autor:in) / Hisano, Daisuke (Autor:in) / Nakayama, Yu (Autor:in)


    Erscheinungsdatum :

    01.05.2020


    Format / Umfang :

    135169 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Traffic Density Estimation using a Convolutional Neural Network

    Nubert, Julian / Truong, Nicholas Giai / Lim, Abel et al. | ArXiv | 2018

    Freier Zugriff

    Traffic Density Estimation and Traffic Control using Convolutional Neural Network

    Ikiriwatte, A.K. / Perera, D.D.R. / Samarakoon, S.M.M.C. et al. | IEEE | 2019