The fast growth of time-sensitive applications calls for the optimization of radio access network (RAN) scheduling. We consider the problem of RAN scheduling of a mix of periodic and burst traffic and design a reinforcement learning method for the age of information and throughput optimization. The periodic traffic is generated with a fixed frequency and the burst traffic is generated by the Poisson Pareto Burst Process. We firstly formulate the scheduling problem as a non-linear integer programming problem. Then, we focus on the reinforcement learning method modeling and solve it via the Proximal Policy Optimization algorithm. Our evaluations show that the suggested reinforcement algorithm outperforms the classical algorithms without any prior knowledge of the arriving traffic.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    AoI and Throughput Optimization for Hybrid Traffic in Cellular Uplink Using Reinforcement Learning


    Beteiligte:


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    443156 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    UAV-Assisted Hybrid Throughput Optimization Based on Deep Reinforcement Learning

    Zhang, Zhilan / Liu, Shuo / Luo, Yizhe et al. | IEEE | 2023



    Traffic Light Control Using Reinforcement Learning

    Masfequier Rahman Swapno, S M / Nuruzzaman Nobel, SM / A C, Ramachandra et al. | IEEE | 2024



    DEEP REINFORCEMENT LEARNING APPROACH TO AIR TRAFFIC OPTIMIZATION USING THE MUZERO ALGORITHM

    Yilmaz, Emre / Sanni, Olatunde / Herniczek, Mark Kotwicz et al. | TIBKAT | 2021