A Practical Validation of RIS Detection and Identification

Reconfigurable intelligent surface (RIS)-assisted communication is a key enabling technology for next-generation wireless communication networks, allowing for the reshaping of wireless channels without requiring traditional radio frequency (RF) active components. While their passive nature makes RISs highly attractive, it also presents a challenge: RISs cannot actively identify themselves to user equipments (UEs). Recently, a new method has been proposed to detect and identify RISs by letting them modulate their identities in the signals reflected from their surfaces. In this letter, we first propose a new and simpler modulation method for RISs and then validate the concept of RIS detection and identification (RIS-ID) using a real-world experimental setup. The obtained results validate the RIS-ID concept and show the effectiveness of our proposed modulation method over different operating scenarios and systems settings.

A Simple Detection and Identification Scheme For Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surface (RIS)-empowered communication is one of the promising physical layer enabling technologies for the sixth generation (6G) wireless networks due to their unprecedented capabilities in shaping the wireless communication environment. RISs are modeled as passive objects that can not transmit or receive wireless signals. While the passiveness of these surfaces is a key advantage in terms of power consumption and implementation complexity, it limits their capability to interact with the other active components in the network. Specifically, unlike conventional base stations (BSs), which actively identify themselves to user equipment (UEs) by periodically sending pilot signals, RISs need to be detected from the UE side. This paper proposes a novel RIS identification (RIS- ID) scheme, enabling UEs to detect and uniquely identify RISs in their surrounding environment. Furthermore, to assess the proposed RIS-ID scheme, we propose two performance metrics: the false and miss detection probabilities. These probabilities are analytically derived and verified through computer simulations, revealing the effectiveness of the proposed RIS-ID scheme under different operating scenarios.