Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.


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    Titel :

    Noise-Robust Feature Combination Method for Modulation Classification Under Fading Channels


    Beteiligte:
    Zhou, Siyang (Autor:in) / Wu, Zhilu (Autor:in) / Yin, Zhendong (Autor:in) / Yang, Zhutian (Autor:in)


    Erscheinungsdatum :

    2018-08-01


    Format / Umfang :

    761788 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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