This paper proposes a simplified enhancement of deep learning-based Joint Source-Channel Coding (Deep JSCC) over multiple-input multiple-output (MIMO) channel. Deep JSCC utilizes trained encoder and decoder weights, allowing for the integrated implementation of source coding and channel coding for batch image transmission. During the training phase, the system is trained based on single-input single-output (SISO) with additive white Gaussian noise (AWGN) communication channel principles. In the testing phase, image signals are transmitted via MIMO channels after passing through the respective Deep JSCC encoder and precoding, and subsequently, these signals are input to the decoder after MIMO detection. We examine fundamental image transmission performance in terms of PSNR and SSIM under various MIMO weight designs.


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    Titel :

    Performance Evaluation of MIMO Transmission in Deep Joint Source-Channel Coding


    Beteiligte:
    Inokuma, Shion (Autor:in) / Sasaki, Yuki (Autor:in) / Hisano, Daisuke (Autor:in) / Nakayama, Yu (Autor:in) / Maruta, Kazuki (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    5286467 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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