In recent years, vehicle-to-vehicle (V2V) communications are used not only for safe driving assistance such as collision avoidance but also for advanced autonomous driving using dynamic maps and remote control. Ultra-reliable low-latency communications (URLLC) are necessary for safety-related functions, e.g. driver assistance and autonomous driving. Due to high mobility, V2V communication must be performed in a variety of communication environments. However, millimeter wave (mmWave) communications, an enabler of URLLC, are greatly affected by the environment, e.g. the terrain, density of vehicles, etc.. This paper proposes an environment-dependent throughput estimation method for mmWave V2X communications. Roadside units (RSUs) collect information on the throughputs of their nearby vehicles and calculate their distribution model with the Bayesian approach. The proposed method provides a regression scheme to estimate distribution models of the throughput of the area without RSUs. The simulation evaluation validates that the beta distribution fits well for the throughput of mmWave vehicular communication and that the regression scheme can estimate throughput models of the area without RSUs.


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    Titel :

    Environment-Dependent Throughput Distribution Estimation Based on Bayesian Approach for mmWave Vehicular Communications


    Beteiligte:
    Kurebayashi, Yuhi (Autor:in) / Taya, Akihito (Autor:in) / Tobe, Yoshito (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    1942775 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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