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Multiagent Deep Reinforcement Learning for Wireless-Powered UAV Networks
IEEE Internet of Things Journal
  • Omar Sami Oubbati, University Gustave Eiffel, LIGM, Marne-la-Vallée, 77454, France
  • Abderrahmane Lakas, United Arab Emirates University, College of Information Technology, Al Ain, United Arab Emirates
  • Mohsen Guizani, Mohamed bin Zayed University of Artificial Intelligence
Document Type

Unmanned aerial vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, as UAVs execute their missions in the sky, it becomes beneficial to wirelessly harvest energy from external and adjustable flying energy sources (FESs) to power their onboard batteries and avoid disrupting their trajectories. For this purpose, wireless power transfer (WPT) is seen as a promising charging technology to keep UAVs in flight and allow them to complete their missions. In this work, we leverage a multiagent deep reinforcement learning (MADRL) method to optimize the task of energy transfer between FESs and UAVs. The optimization is performed by carrying out three essential tasks: 1) maximizing the sum-energy received by all UAVs based on FESs using WPT; 2) optimizing the energy loading process of FESs from a ground BS; and 3) computing the most energy-efficient trajectories of the FESs while carrying out their charging duties. Furthermore, to ensure high-level reliability of energy transmission, we use directional energy transfer for charging both FESs and UAVs by using laser beams and energy beam-forming technologies, respectively. In this study, the simulation results show that the proposed MADRL method has efficiently optimized the trajectories and energy consumption of FESs, which translates into a significant energy transfer gain compared to the baseline strategies. © 2022 IEEE.

Publication Date
  • Deep reinforcement learning (DRL),
  • wireless power transfer (WPT),
  • Antennas,
  • Deep learning,
  • Energy efficiency,
  • Energy harvesting,
  • Energy utilization,
  • Inductive power transmission,
  • Internet of things,
  • Laser beams,
  • Multi agent systems,
  • Reinforcement learning,
  • Trajectories,
  • Unmanned aerial vehicles (UAV),
  • Wireless sensor networks

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OA version (pathway a): Accepted version

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Citation Information
O. S. Oubbati, A. Lakas and M. Guizani, "Multiagent Deep Reinforcement Learning for Wireless-Powered UAV Networks," in IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16044-16059, 1 Sept.1, 2022, doi: 10.1109/JIOT.2022.3150616.