The importance of vehicular communications has grown significantly in recent years. Potential use cases of vehicular communications are manifold and range from sharing information for driver assistance to entertainment purposes. This means that each connected vehicle has an individual data requirement for the communication infrastructure. However, due to the dynamic wireless environment, the simultaneous fulfillment of such requirements cannot be guaranteed. Therefore, novel solutions should not only consider the requirements of each user but also the risk of not being able to fulfill them. In this paper, we consider a vehicular communication scenario consisting of a base station that serves the vehicles in its coverage area using 5G millimeter wave (mmWave) narrow beams. The problem boils down to finding an optimal policy for the selection of the narrow beams. This should be done carefully, as the choice of the used beams greatly impacts the performance. For this purpose, we propose a risk-aware contextual Multi-Armed Bandit (MAB) online learning algorithm. Using this algorithm, the base station autonomously learns its environment and selects the best set of beams based on the vehicles located in its coverage area. In order to achieve a large risk awareness, this work focuses on two pillars. Firstly, the notion of risk is integrated in the proposed contextual MAB algorithm by exploiting the concepts of Mean-Variance and Conditional Value at Risk for the evaluation of the decisions made by the algorithm. Secondly, we introduce mechanisms that can detect non-stationarities and swiftly adapt to them in order to make the proposed approach robust against volatile environments that violate stationarity assumptions. By using extensive simulations, the effectiveness of the aforementioned approaches are proven numerically.


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    Titel :

    Risk-Aware Multi-Armed Bandits for Vehicular Communications


    Beteiligte:
    Wirth, Maximilian (Autor:in) / Klein, Anja (Autor:in) / Ortiz, Andrea (Autor:in)


    Erscheinungsdatum :

    2022-06-01


    Format / Umfang :

    1764983 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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