In this paper, we propose a low-complexity graphic constellation projection (GCP) algorithm for automatic modulation classification (AMC), where the recovered symbols are projected into artificial graphic constellations. Unlike the existing feature- based (FB) algorithms, we convert the AMC problem into an image recognition problem. Subsequently, the deep belief network (DBN) is adopted to learn the underlying features in these constellations and recognize their corresponding modulation schemes. Simulation results demonstrate that the proposed GCP-DBN based AMC system achieves better performance than other schemes. Specifically, the classification accuracy is beyond 95% at 0 dB, which is very close to the average likelihood ratio test upper bound (ALRT-Upper Bound).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Graphic Constellations and DBN Based Automatic Modulation Classification


    Beteiligte:
    Wang, Fen (Autor:in) / Wang, Yongchao (Autor:in) / Chen, Xi (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    344583 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Trellis-coded modulation with optimized signal constellations

    Van der Vleuten, R. J. / Weber, J. H. / IEEE; Vehicular Technology/Communications Society et al. | British Library Conference Proceedings | 1993


    Statistical Classification of Remote Sensing Satellite Constellations

    Sanad, Ibrahim / Vali, Zahra / Michelson, David G. | IEEE | 2020


    Constellations

    Criswell, D. R. | NTRS | 1985


    Adaptive Automatic Gain Control for Nonlinearly, Distorted Constellations

    Grayver, Eugene / McDonald, Eric / Ardestani, David | IEEE | 2007