RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Journal: Sensors (Basel, Switzerland)
Published Date: Aug 16, 2022

Abstract

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods.

Authors

  • Feng Jing
    School of Telecommunication Engineering, Xidian University, Xi'an 710126, China.
  • Hailin Zhang
  • Mei Gao
    Department of Anesthesiology and Pain Medicine, University of California Davis Health System, Sacramento, CA; Department of Anesthesiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
  • Bin Xue
    Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, FL 33620, USA. binxue@usf.edu.
  • Kunrui Cao
    School of Information and Communication, National University of Defense Technology, Xi'an 430035, China.

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