Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vision-aided Multi-user Beam Tracking for mmWave Massive MIMO System: Prototyping and Experimental Results


    Contributors:
    Li, Kehui (author) / Zhou, Binggui (author) / Guo, Jiajia (author) / Yang, Xi (author) / Xue, Qing (author) / Gao, Feifei (author) / Ma, Shaodan (author)


    Publication date :

    2024-06-24


    Size :

    2165436 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Beam Squint Effect in Multi-Beam mmWave Massive MIMO Systems

    Afeef, Liza / Arslan, Huseyin | IEEE | 2022


    Sensing-aided Multi-modal Channel Prediction in 6G mmWave Massive MIMO Systems

    Moon, Jihoon / Ngo, Khoa Anh / Shim, Byonghyo | IEEE | 2024



    mmWave massive MIMO vehicular communications

    Cheng, Xiang / Gao, Shijian / Yang, Liuqing | TIBKAT | 2023


    Matrix Normalization Based ZF Hybrid Precoded Multi-User MIMO mmWave Systems with Massive Array

    Li, Hang / Wang, Thomas Q. / Huang, Xiaojing et al. | IEEE | 2018