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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    Title :

    Semantic Communication System Based on Convolutional Neural Networks


    Contributors:
    Wang, Jiawei (author) / Jia, Xiaohui (author) / Deng, Keyan (author)


    Publication date :

    2023-10-11


    Size :

    2173616 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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