In this paper, we propose an untrained deep image prior-based compressive channel estimation (CE) algorithm in massive MIMO systems. We adopt deep convolutional generative network (DCGN) to learn the sparse prior of massive MIMO channels, whose weights can be optimized to match the compressed measurements. Unlike various supervised deep neural networks (DNN)-based CE algorithms, which require offline training over large simulated channel datasets, our DCGN does not require pre-training and can be learned online based on the real-time measurements to make it adaptable to the time-varying channels. Besides, DCGN can exploit the channel structure automatically without any prior knowledge. We further devise a learned regularization technique to improve the CE performance when the measurements are noisy and highly compressive. Simulations show that the proposed method can achieve more accurate and robust online CE performance than traditional compressive sensing (CS) and DNN-based methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Online Compressive Channel Learning Using Untrained Deep Generative Model


    Beteiligte:
    Wang, Ben (Autor:in) / Lian, Lixiang (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    548565 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    ERROR MITIGATION FOR UNTRAINED DATA UTILIZING GENERATIVE MODEL IN VEHICLE SHAPE ESTIMATION WITH MILLIMETER-WAVE RADAR BY DEEP NEURAL NETWORK

    Akita, Tokihiko / Kyutoku, Haruya / Tanikawa, Ukyo et al. | British Library Conference Proceedings | 2021


    UNTRAINED SYSTEMS AND METHODS FOR VEHICLE SPEED ESTIMATION

    REVAUD JÉROME / CABON YOHANN | Europäisches Patentamt | 2023

    Freier Zugriff

    Untrained systems and methods for vehicle speed estimation

    REVAUD JÉROME / CABON YOHANN | Europäisches Patentamt | 2023

    Freier Zugriff

    Driver Identification Using Deep Generative Model With Limited Data

    Hu, Hongyu / Liu, Jiarui / Chen, Guoying et al. | IEEE | 2023