Reconfigurable Intelligent Surface based RF Sensing: Design, Optimization, and Implementation

Jingzhi Hu, Hongliang Zhang, Boya Di, Lianlin Li, Kaigui Bian, Lingyang Song, Yonghui Li, Zhu Han, H. Vincent Poor

Research output: Contribution to journalArticle

Abstract

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

Original languageEnglish (US)
JournalIEEE Journal on Selected Areas in Communications
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Reconfigurable intelligent surface
  • posture recognition
  • radio-frequency sensing

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