When ground base stations are not available in the aftermath of a disaster, unmanned aerial vehicle (UAV) acting as flying relay is a promising option. The UAVs with limited energy as flying relays allow for wider data coverage and more stable data transmission. However, with the changes of ground devices topology and channel, it is challenging to consider quality of service (QoS) and the age of information (AoI) in UAV communication under the energy constraint. In this paper, we propose a dynamic priority packet scheduling for UAV assisted AoI-aware network whose utility is maximized subject to QoS to get the best tradeoff of the energy consumption and the weighted AoI. Specifically, the dynamics of devices are characterized by Gauss-Markov mobility model. Dynamic priority is affected by devices' movement, channel changes and others. We optimize the trajectory of the UAV and the scheduling scheme of the packets by the Dueling Double Deep Q Network (D3QN) algorithm. Simulations show that the scheme significantly improves the utility of the system compared to the benchmarks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Dynamic Priority Packet Scheduling for UAV Assisted AoI-Aware Network: A Deep Reinforcement Learning Approach


    Beteiligte:
    Fu, Xiaoying (Autor:in) / Miao, Jiansong (Autor:in) / Yao, Yushun (Autor:in) / Zhang, Tao (Autor:in) / Bai, Shanling (Autor:in) / Yi, Lan (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    884142 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

    Goh, Edwin / Venkataram, Hamsa Shwetha / Hoffmann, Mark et al. | IEEE | 2021


    Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

    Wilson, Brian / Johnston, Mark D. / Hoffmann, Mark et al. | NTRS | 2021


    AoI-Aware Dynamic User Scheduling in Vehicular Networks Based on Soft Reinforcement Learning

    Huang, Zhisen / Guo, Chongtao / Chen, Jiayi et al. | IEEE | 2023