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.
Hierarchical Multi-Agent Reinforcement Learning-Based UAV Control for Wireless Covert Communications
10.01.2025
2050135 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Multi-hop UAV relay covert communication: A multi-agent reinforcement learning approach
Elsevier | 2025
|Tema Archiv | 1977
|DOAJ | 2025
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