Recent years have witnessed a surge of developments in deep learning (DL) motivated by a variety of contemporary applications. The conventional DL-based automatic modulation classification (AMC) methods are always relying on a great quantity of data. In this article, we propose a DL-based AMC model with short data for the spectrum sensing of wireless communication signals. First, a hopping transform unit is proposed to represent the transient variation occurred either by frequency, amplitude, or phase modulations. Second, a bidirectional long short-term memory-based hopping feature perception model, namely deep-learning hopping capture model (DHCM), is built for the AMC. A comprehensive comparison of the DHCM with other existing methods is then provided under various signal-to-noise ratios. The experimental results demonstrate the superiority of the proposed method under short data.


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    Title :

    Deep-Learning Hopping Capture Model for Automatic Modulation Classification of Wireless Communication Signals


    Contributors:
    Li, Lin (author) / Dong, Zhiyuan (author) / Zhu, Zhigang (author) / Jiang, Qingtang (author)


    Publication date :

    2023-04-01


    Size :

    4232209 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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