Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation

TL;DR

This paper introduces a novel RIS-aided wireless sensing framework for high-resolution scene depth estimation, addressing limitations of optical methods and demonstrating promising simulation results compared to RGB-based approaches.

Contribution

It develops a comprehensive RIS-based mmWave sensing framework with a new interaction codebook for efficient depth map construction, advancing wireless sensing capabilities.

Findings

RIS-aided sensing achieves high-resolution depth maps

The proposed codebook improves sensing efficiency

Simulation shows competitive performance against RGB methods

Abstract

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.