Semantic communication which breaks the Shannon limit in the existing communication field is considered as a promising technology. In this study, we present a novel semantic communication system that utilizes a convolutional neural network(CNN) architecture. The system incorporates dual convolutional layers, dual pooling layers, and a fully connected layer to facilitate semantic encoding. To evaluate the performance of the proposed system, we conduct experiments using the MNIST dataset. The experiment results demonstrate that 99.1% accuracy in text information can be obtained.


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    Titel :

    Semantic Communication System Based on Convolutional Neural Networks


    Beteiligte:
    Wang, Jiawei (Autor:in) / Jia, Xiaohui (Autor:in) / Deng, Keyan (Autor:in)


    Erscheinungsdatum :

    11.10.2023


    Format / Umfang :

    2173616 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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