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Zero-Power Backscatter Sensing and Communication Proof-of-Concept

Yu Zhang, Xiaoyu Shi, Tongyang Xu

TL;DR

The paper demonstrates a zero-power RFID backscatter platform as an ISAC proof-of-concept, evaluating both communication and RSSI-based sensing across varied channels and substrates. It presents a dual theoretical framework: backscatter communication mechanics with end-to-end power and LOS path-loss models, and an RTI-based sensing approach using an ellipsoid-weight scheme and Tikhonov-regularized LS recovery. Experimentally, the authors show that substrate materials and environmental conditions strongly affect RSS, with wall attachments causing severe degradation (RSS dropping near the $-84$ dBm threshold in NLOS), while LOS offers robust reception; RTI successfully detects and localizes human motion. These findings inform practical deployment of low-power RFID ISAC systems in heterogeneous environments, highlighting material effects and the utility of RTI for motion sensing.

Abstract

In this paper, we present an experimental setup to evaluate the performance of a radio frequency identification (RFID)-based integrated sensing and communication (ISAC) system. We focus on both the communication and sensing capabilities of the system. Our experiments evaluate the system's performance in various channel fading scenarios and with different substrate materials, including wood, plastic, wall, and glass. Additionally, we utilize radio tomographic imaging (RTI) to detect human motion by analyzing received signal strength indicator (RSSI) data. Our results demonstrate the impact of different materials and environments on RSSI and highlight the potential of RFID-based systems for effective sensing and communication in diverse applications.

Zero-Power Backscatter Sensing and Communication Proof-of-Concept

TL;DR

The paper demonstrates a zero-power RFID backscatter platform as an ISAC proof-of-concept, evaluating both communication and RSSI-based sensing across varied channels and substrates. It presents a dual theoretical framework: backscatter communication mechanics with end-to-end power and LOS path-loss models, and an RTI-based sensing approach using an ellipsoid-weight scheme and Tikhonov-regularized LS recovery. Experimentally, the authors show that substrate materials and environmental conditions strongly affect RSS, with wall attachments causing severe degradation (RSS dropping near the dBm threshold in NLOS), while LOS offers robust reception; RTI successfully detects and localizes human motion. These findings inform practical deployment of low-power RFID ISAC systems in heterogeneous environments, highlighting material effects and the utility of RTI for motion sensing.

Abstract

In this paper, we present an experimental setup to evaluate the performance of a radio frequency identification (RFID)-based integrated sensing and communication (ISAC) system. We focus on both the communication and sensing capabilities of the system. Our experiments evaluate the system's performance in various channel fading scenarios and with different substrate materials, including wood, plastic, wall, and glass. Additionally, we utilize radio tomographic imaging (RTI) to detect human motion by analyzing received signal strength indicator (RSSI) data. Our results demonstrate the impact of different materials and environments on RSSI and highlight the potential of RFID-based systems for effective sensing and communication in diverse applications.

Paper Structure

This paper contains 14 sections, 7 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Theoretical Model
  3. Backscatter Communication Mechanism
  4. RTI-based Sensing Mechanism
  5. Experiment Design
  6. Experiment Setup and RFID Devices
  7. Experiment Scenarios
  8. Data Communications
  9. Human Motion Sensing
  10. Substrate Materials
  11. Data Collection
  12. Experiment Results
  13. conclusion
  14. Acknowledgement

Figures (8)

  • Figure 1: The link between a reader and a tag in an RTI network travels in a direct LOS path. The darkened grids represent the non-zero weight coefficients for this link.
  • Figure 2: Experiment hardware: reader, antenna, and tag.
  • Figure 3: Experiment setup for human motion detection.
  • Figure 4: Experiment setup for different channel models. The tag could be attached to the wall or other materials.
  • Figure 5: Tags attached to different materials.
  • ...and 3 more figures