The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.


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    Titel :

    RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems


    Beteiligte:
    Karakoca, Erhan (Autor:in) / Nayir, Hasan (Autor:in) / Gorcin, Ali (Autor:in) / Qaraqe, Khalid (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    2401627 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch