The safety in autonomous driving depends on reliable and efficient sharing of massive sensor data via the internet of vehicles (IoV). In the upcoming sixth-generation (6G) mobile communication network, joint communication and sensing (JCAS) on millimeter wave (mmWave) bands has become a representative technique to achieve the dual functions of ultra-broadband communications and high-precision sensing at the same hardware and software costs. However, under high-mobility IoV,directional mmWave beams cannot provide robust network coverage. Based on this observation, in this paper we devise a sub-6GHz and mmWave integrated IoV wireless network architecture, where the sub-6GHz bands carry control information, and the mmWave bands perform large-volume communication and driving environment sensing. Each vehicle operates two mmWave beams respectively to realize communication and environment sensing. Then, we analyze the inter-beam interference between communication and sensing beams, and propose a Q-learning based beam resource allocation scheme to alleviate the interference problem. Simulation results demonstrate that our proposed scheme can highly improve the mmWave communication and sensing performance.


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    Titel :

    Q-Learning Based mmWave Beam Adjustment for Joint Communication and Sensing Under IoV Wireless Networks


    Beteiligte:
    Yue, Tao (Autor:in) / Yan, Li (Autor:in) / Fang, Xuming (Autor:in) / Li, Yi (Autor:in) / Xue, Qing (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1197696 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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