In this paper, we propose two low-complexity automatic modulation classification (AMC) classifiers based on order-statistics: the linear support vector machine (LSVM) and the approximate maximum likelihood (AML). Specifically, LSVM applies the linear combination of the entire order-statistics of the received signals for the classification, while AML resorts to the asymptotic distribution of the reduced order- statistics to decrease the computational complexity. The Simulations show that the performance of our proposed classifiers is close to that of the maximum likelihood (ML) classifier and outperforms the Kolmogorov-Smirnov (KS) and cumulant-based classifiers. While the complexity of our proposed classifiers is much lower than that of the ML classifier.


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    Titel :

    Low Complexity Automatic Modulation Classification Based on Order Statistics


    Beteiligte:
    Han, Lubing (Autor:in) / Xue, Haozhou (Autor:in) / Gao, Feifei (Autor:in) / Li, Zan (Autor:in)


    Erscheinungsdatum :

    2016-09-01


    Format / Umfang :

    249813 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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