This paper studies an unmanned aerial vehicle (UAV)-enabled wireless power communication networks (WPCN-s), where the UAV provides energy for mobile user nodes (M-UNs) and receives information from M-UNs. The movement of M-UN complies with a Gauss-Markov random model. To ensure acceptable quality-of-service (QoS), we consider dynamically planning the flight path of the UAV according to the movements of M-UNs. Since the flight time of UAV is restricted by limited energy, nonorthogonal multiple access (NOMA) is adopted to access a large number of M-UNs for simultaneous information transmission. Based on the above considerations, we aim to maximize the throughput via path planning of the UAV, subject to the QoS requirements of M-UNs and the UAV's energy constraint. To handle the challenges brought by dynamically changing channels to solving the problem, we propose a QoS-based double deep Q-learning network (DDQN). Numerical simulation results show that, compared with the conventional algorithms, the proposed framework achieves higher throughput.


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    Titel :

    Double deep Q-learning network-based path planning in UAV-assisted wireless powered NOMA communication networks


    Beteiligte:
    Lei, Ming (Autor:in) / Fowler, Scott (Autor:in) / Wang, Juzhen (Autor:in) / Zhang, Xingjun (Autor:in) / Yu, Bocheng (Autor:in) / Yu, Bin (Autor:in)


    Erscheinungsdatum :

    01.09.2021


    Format / Umfang :

    418818 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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