Sensing Aided Reconfigurable Intelligent Surfaces for 3GPP 5G Transparent Operation

TL;DR

This paper explores the use of sensing-enabled reconfigurable intelligent surfaces (RISs) for 5G networks, proposing a machine learning framework that enables RISs to operate transparently and independently, reducing control overhead and simplifying deployment.

Contribution

It introduces a novel sensing and machine learning-based approach for standalone RIS operation in 5G, addressing the challenge of beam direction determination without network assistance.

Findings

Accurately predicts BS and UE-side beams using sensing data.

Achieves near-optimal data rates with reduced beam training overhead.

Demonstrates feasibility of transparent RIS operation in 5G.

Abstract

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.