Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation

This is a MATLAB code package related to the following article: "Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation", accepted to the IEEE International Conference on Communication (ICC), 2023.

Abstract of the Article

Current scene depth estimation approaches mainly rely on optical sensing, which carries privacy concerns and suffers from estimation ambiguity for distant, shiny, and transparent surfaces/objects. Reconfigurable intelligent surfaces (RISs) provide a path for employing a massive number of antennas using low-cost and energy-efficient architectures. This has the potential for realizing RIS-aided wireless sensing with high spatial resolution. In this paper, we propose to employ RIS-aided wireless sensing systems for scene depth estimation. We develop a comprehensive framework for building accurate depth maps using RIS-aided mmWave sensing systems. In this framework, we propose a new RIS interaction codebook capable of creating a sensing grid of reflected beams that meets the desirable characteristics of efficient scene depth map construction. Using the designed codebook, the received signals are processed to build high-resolution depth maps. Simulation results compare the proposed solution against RGB-based approaches and highlight the promise of adopting RIS-aided mmWave sensing in scene depth perception.

Code Package Content

Data preparation

• The data used in this package can be found in this Dropbox folder. Please download these files to the current repository.
• The Blender and Wireless Insite files of the samples can be found in this Dropbox folder.

RIS-based depth estimation

• Run main.m to generate the results.
• Run estimate_error.m to compute the estimation error and plot the figure.

If you have any questions regarding the code, please contact Hao Luo.

Authors

• Abdelrahman Taha, Wireless Intelligence Lab, Arizona State University
• Hao Luo, Wireless Intelligence Lab, Arizona State University

License and Referencing

This code package is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

If you in any way use this code for research that results in publications, please cite our original article:

A. Taha, H. Luo and A. Alkhateeb, "Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation," ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 491-497.