High-Resolution WiFi Imaging With Reconfigurable Intelligent Surfaces

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 the 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 article, we propose an 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.

Automated summary

WiFi-based imaging achieves pervasive sensing by protecting privacy and reducing cost. However, most existing methods either require specialized hardware modification or suffer from poor imaging performance due to the limited spatial resolution of off-the-shelf WiFi devices. This article proposes an RIS-aided WiFi imaging framework to achieve high-resolution imaging with off-the-shelf WiFi devices. The framework includes a beamforming method to separate signals from different spatial locations with the aid of the RIS, and an optimization-based super-resolution imaging algorithm that considers the effect of finite phase quantization in RIS. Simulation results show that the framework achieves a median RMSE of 0.03 and median SSIM of 0.52.