Unmanned Aerial Vehicles (UAVs) are increasingly employed across diverse sectors for their exceptional agility, adaptability, and swift deployment. This research delves into the application of UAVs as relay nodes within expansive cellular networks, where ground sensors (GSs) transmit their packets to ground base stations (GBSs) via multiple UAV relays. To ensure UAVs' safe navigation and consistent connectivity with GBSs, we introduce a constraint on the control link's outage time between a UAV and its corresponding GBS during flight. The focus of this paper is on the design of UAVs' trajectory and relaying strategy to minimize the overall mission completion duration, taking into account energy usage limitations and UAV control link outage constraints. Leveraging the collaboration of multiple UAVs, we propose a QMIX-based UAV relaying strategy and assess its efficacy via comprehensive simulations. Comparing with two benchmark algorithms, our proposed algorithm demonstrates a reduction in mission completion time and an increase in overall reward.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    UAV-Assisted Relay Communication: A Multi-Agent Deep Reinforcement Learning Approach


    Beteiligte:
    Huang, Longqian (Autor:in) / Sun, Hongguang (Autor:in) / Gao, Yinjie (Autor:in) / Zhang, Hongming (Autor:in) / Li, Shuqin (Autor:in)


    Erscheinungsdatum :

    20.12.2024


    Format / Umfang :

    12195356 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-hop UAV relay covert communication: A multi-agent reinforcement learning approach

    BAI, Hengzhi / WANG, Haichao / HE, Rongrong et al. | Elsevier | 2025

    Freier Zugriff

    Coordination and communication in deep multi-agent reinforcement learning

    Schroeder de Witt, CA | BASE | 2022

    Freier Zugriff

    A Multi-Agent Deep Reinforcement Learning Approach for Traffic Management in Complex Communication Networks

    Alhachem, Christina / Kellil, Mounir / Bouabdallah, Abdelmadjid | IEEE | 2025



    Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions

    Liao, Pei-Hsiang / Shen, Li-Hsiang / Wu, Po-Chen et al. | IEEE | 2024