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.


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    Title :

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


    Contributors:
    Igbafe Orikumhi (author) / Jungsook Bae (author) / Hyunwoo Park (author) / Sunwoo Kim (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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