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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


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

    Published in:

    Publication date :

    2024-11-01


    Size :

    3935541 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    BsNet: A Deep Learning-Based Beam Selection Method for mmWave Communications

    Lin, Chia-Hung / Kao, Wei-Cheng / Zhan, Shi-Qing et al. | IEEE | 2019


    Robust Beam Tracking Algorithm for mmWave MIMO Systems in Mobile Environments

    Kim, Seonyong / Han, Hyungsik / Kim, Namshik et al. | IEEE | 2019



    MmWave Vehicular Beam Alignment Leveraging Online Learning

    Xian, Qingyang / Doufexi, Angela / Armour, Simon | IEEE | 2023