Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling.Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the-of-coverage area.Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.


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    Titel :

    Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage


    Beteiligte:
    Sahin, Taylan (Autor:in) / Khalili, Ramin (Autor:in) / Boban, Mate (Autor:in) / Wolisz, Adam (Autor:in)


    Erscheinungsdatum :

    01.12.2018


    Format / Umfang :

    695596 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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