  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.
