In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    DRL-based Multi-UAV trajectory optimization for ultra-dense small cells


    Beteiligte:
    Igbafe Orikumhi (Autor:in) / Jungsook Bae (Autor:in) / Hyunwoo Park (Autor:in) / Sunwoo Kim (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells

    Wang, Li-Chun / Cheng, Shao-Hung / Tsai, Ang-Hsun | IEEE | 2016


    Learning Interaction-Aware Guidance for Trajectory Optimization in Dense Traffic Scenarios

    Brito, Bruno / Agarwal, Achin / Alonso-Mora, Javier | IEEE | 2022


    GSOANR-based multi-objective train trajectory optimization

    Li, Wei / Zhao, Sizhe / Li, Kang et al. | Taylor & Francis Verlag | 2024


    Multi-objective Trajectory Optimization Problem

    Chai, Runqi / Savvaris, Al / Tsourdos, Antonios et al. | Springer Verlag | 2019


    Study on Scheduling Techniques for Ultra Dense Small Cell Networks

    Jafari, Amir H. / Lopez-Perez, David / Ding, Ming et al. | IEEE | 2015