Deep reinforcement learning for physical layer security enhancement in energy harvesting based cognitive radio networks

HIGHLIGHTS

• who: Ruiquan Lin and collaborators from the College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China have published the Article: Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks, in the Journal: Sensors 2023, 23, 807. of /2023/
• what: Unlike the research that only focuses on a traditional reinforcement learning algorithm, Ref proposes a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for maximizing the secrecy capacity by joint optimization of the UAVs` trajectory, the transmission power, and the jamming power. Motivated by the prior works, the authors . . .
  

   

  

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