  Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode
that is completely transparent to the 3GPP 5G initial access process? Realizing
that may greatly simplify the deployment and operation of these surfaces and
reduce the infrastructure control overhead. This paper investigates the
feasibility of building standalone/transparent RIS systems and shows that one
key challenge lies in determining the user equipment (UE)-side RIS beam
reflection direction. To address this challenge, we propose to equip the RISs
with multi-modal sensing capabilities (e.g., using wireless and visual sensors)
that enable them to develop some perception of the surrounding environment and
the mobile users. Based on that, we develop a machine learning framework that
leverages the wireless and visual sensors at the RIS to select the optimal
beams between the base station (BS) and users and enable 5G
standalone/transparent RIS operation. Using a high-fidelity synthetic dataset
with co-existing wireless and visual data, we extensively evaluate the
performance of the proposed framework. Experimental results demonstrate that
the proposed approach can accurately predict the BS and UE-side candidate
beams, and that the standalone RIS beam selection solution is capable of
realizing near-optimal achievable rates with significantly reduced beam
training overhead.
