Camera Aided Reconfigurable Intelligent Surfaces: Computer Vision Based Fast Beam Selection

TL;DR

This paper introduces a vision-aided machine learning approach for reconfigurable intelligent surfaces that uses visual sensors to efficiently select optimal beams, reducing training overhead and maintaining high communication rates.

Contribution

It proposes a novel ML framework that leverages visual data from cameras on RIS to guide beam selection, significantly reducing training overhead in mmWave systems.

Findings

Accurately predicts RIS beams using visual data.

Achieves near-optimal communication rates.

Reduces beam training overhead substantially.

Abstract

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems. However, designing the RIS beamforming typically requires large channel estimation or beam training overhead, which degrades the efficiency of these systems. In this paper, we propose to equip the RIS surfaces with visual sensors (cameras) that obtain sensing information about the surroundings and user/basestation locations, guide the RIS beam selection, and reduce the beam training overhead. We develop a machine learning (ML) framework that leverages this visual sensing information to efficiently select the optimal RIS reflection beams that reflect the signals between the basestation and mobile users. To evaluate the developed approach, we build a high-fidelity synthetic dataset that comprises co-existing wireless and visual data. Based on this dataset, the results show that the proposed vision-aided machine learning solution can accurately predict the RIS beams and achieve near-optimal achievable rate while significantly reducing the beam training overhead.