Millimeter wave (mmWave) techniques have attracted much attention in recent years owing to features such as substantial bandwidth for communication, and it has applications in radar systems and location applications. To compensate for the severe path loss in mmWave bands, beamforming techniques with a massive antenna array are usually employed to provide high directivity. However, the resulting high-gain and narrow pencil beam make the beam alignment costlier and much more difficult. Hence, conducting beam alignment with a low overhead becomes critical. Herein, we propose a promising solution that does not require channel knowledge and treats the beam selection as an image reconstruction problem; thus, deep neural networks can be employed to operate the beam domain image reconstruction. This approach can be divided into two stages: off-line training and on- line prediction. The overhead of the on-line beam selection can be significantly reduced via off- line Eigen-beam extraction without degrading the beamforming performance. Simulations are conducted to confirm the performance of the proposed framework in scalability and robustness.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2019-09-01


    Size :

    755025 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Spatio-temporal Beam Training for mmWave Communications with Human Self-blockage

    Shan, Wenxing / Ma, Yiming / Wang, Zicun et al. | IEEE | 2023


    On Spectrum Sensing for mmWave and THz Beam-based Communications

    Zang, Junwei / Liu, Qiao / He, Jia et al. | IEEE | 2023


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

    Zhong, Weizhi / Zhang, Lulu / Jin, Haowen et al. | IEEE | 2024


    Machine-Learning-Based Throughput Estimation Using Images for mmWave Communications

    Okamoto, Hironao / Nishio, Takayuki / Morikura, Masahiro et al. | IEEE | 2017


    MmWave Vehicular Beam Alignment Leveraging Online Learning

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