High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces a novel RIS-aided WiFi imaging framework that significantly improves spatial resolution using off-the-shelf WiFi devices, combining beamforming and super-resolution algorithms.

Contribution

The work presents a new RIS-based approach for high-resolution WiFi imaging, including a beamforming method and an optimization algorithm that accounts for phase quantization effects.

Findings

Achieves median RMSE of 0.03 in simulations

Attains median SSIM of 0.52 in simulations

Demonstrates high-resolution imaging at 5 GHz with commercial WiFi protocols

Abstract

WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root mean square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.