Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement Learning

Haoran Peng, Li Chun Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Integrating unmanned aerial vehicles with RIS (UAV–RIS) can offer ubiquitous deployment services in communication-disabled areas, but is limited by the on-board energy of the UAVs. In this paper, a novel energy harvesting (EH) scheme on top of the UAV–RIS system, called EH-RIS scheme, is developed for the next generation high performance wireless system. The proposed EH-RIS scheme extends the simultaneous wireless information and power transfer (SWIPT) system by splitting the passive reflected arrays on the geometric space for transporting information and harvesting energy simultaneously. However, pedestrian mobility, and rapid channel changes post challenges to efficient resource allocation in wireless systems. Thus, a robust deep reinforcement learning (DRL)-based algorithm is developed to improve the proposed EH-RIS scheme for guaranteeing the quality of service (QoS) under dynamic wireless environments. The simulation results demonstrate the effectiveness and efficiency of the proposed robust DRL-based EH-RIS system, which not only outperform the existing state-of-the-art solutions but also approach to the performance of the exhaustive search method.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Wireless Communications
StateAccepted/In press - 2023


  • Energy efficiency
  • Energy Harvesting
  • Energy harvesting
  • Optimization
  • Quality of service
  • Reconfigurable Intelligent Surface
  • Reinforcement learning
  • Resource management
  • Unmanned Aerial Vehicle
  • Wireless communication


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