In this study, we consider wireless covert communication within unmanned aerial vehicle (UAV) environments. Here, the UAV functions as a covert transmitter, sending data to predetermined ground receivers while avoiding detection by ground-based detectors. We aim to maximize the UAVs' through-put and the detector's minimum detection error probability by optimizing the UAV's transmission power and positioning through Q-learning. We utilize reinforcement learning to de-termine UAVs' optimal transmission power and location in complex environments, ensuring effective problem-solving even in challenging scenarios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hierarchical Multi-Agent Reinforcement Learning-Based UAV Control for Wireless Covert Communications


    Beteiligte:
    Seong, Hayoung (Autor:in) / Kim, Taewook (Autor:in) / Song, Jungsuk (Autor:in) / Lee, Howon (Autor:in)


    Erscheinungsdatum :

    10.01.2025


    Format / Umfang :

    2050135 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

    Covert communications

    Crepeau, P.J. | Tema Archiv | 1977


    Mitigating Bus Bunching via Hierarchical Multi-Agent Reinforcement Learning

    Yu, Mengdi / Yang, Tao / Li, Chunxiao et al. | IEEE | 2024


    Simultaneous Beamforming and Nullforming for Covert Wireless Communications

    Kong, Justin / Dagefu, Fikadu T. / Sadler, Brian M. | IEEE | 2020


    Robust UAV-Oriented Wireless Communications via Multi-Agent Deep Reinforcement Learning to Optimize User Coverage

    Mahfizur Rahman Khan / Gowtham Raj Veeraswamy Premkumar / Bryan Van Scoy | DOAJ | 2025

    Freier Zugriff