Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.


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    Titel :

    Image-Based Beam Tracking With Deep Learning for mmWave V2I Communication Systems


    Beteiligte:
    Zhong, Weizhi (Autor:in) / Zhang, Lulu (Autor:in) / Jin, Haowen (Autor:in) / Liu, Xin (Autor:in) / Zhu, Qiuming (Autor:in) / He, Yi (Autor:in) / Ali, Farman (Autor:in) / Lin, Zhipeng (Autor:in) / Mao, Kai (Autor:in) / Durrani, Tariq S. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    3935541 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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