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.


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    Title :

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


    Contributors:
    Seong, Hayoung (author) / Kim, Taewook (author) / Song, Jungsuk (author) / Lee, Howon (author)


    Publication date :

    2025-01-10


    Size :

    2050135 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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