The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles’ positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.


    Access

    Download


    Export, share and cite



    Title :

    Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks


    Contributors:
    Yan Li (author) / Ding Haichuan (author) / Zhang Lan (author) / Fang Xuming (author) / Fang Yuguang (author) / Xiao Ming (author)

    Publication date :

    2019-10-05


    Remarks:

    oai:zenodo.org:4242959
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 18(10) 4873-4885



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    006 / 629




    Light-weight Machine Learning for mmWave Vehicular Communications

    Wang, Yitu / Nakachi, Takayuki | IEEE | 2020


    Exploring software defined networks for seamless handovers in vehicular networks

    Silva, Miguel / Teixeira, Pedro / Gomes, Christian et al. | BASE | 2023

    Free access

    MACHINE LEARNING CONTROL OF OBJECT HANDOVERS

    WAY JAN / CHRISTOPHER JASON PAXTON / CHAO YU-WEI et al. | European Patent Office | 2022

    Free access

    Mmwave Beam Management in Urban Vehicular Networks

    Zana Limani Fazliu / Francesco Malandrino / Carla Fabiana Chiasserini et al. | BASE | 2021

    Free access

    MmWave Vehicular Beam Alignment Leveraging Online Learning

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