SORIS: A Self-Organized Reconfigurable Intelligent Surface Architecture for Wireless Communications

TL;DR

The paper addresses the challenge of acquiring instantaneous CSI at the RIS with low power and latency by proposing SORIS, a self-organized RIS where a microcontroller receiver behind the metasurface estimates the BS–RIS and RIS–UE channels using a subset of transmitting-mode elements and predicts the rest via ML, reducing backhaul feedback requirements. The approach combines a dual-mode RIS architecture, a two-phase channel acquisition protocol, and an ML-driven extrapolation that leverages spatial correlation among RIS elements, with a thorough complexity, wiring-density, and control-signaling analysis. Key contributions include the novel hardware design, the ML-based CSI prediction engine, and guidelines for selecting active transmit elements, validated by Monte Carlo simulations under various configurations. The work demonstrates the potential for real-time RIS adaptation with a single RF chain, offering practical implications for energy efficiency and scalable deployment in 6G/mmWave systems.

Abstract

In this paper, a new reconfigurable intelligent surface (RIS) hardware architecture, called self-organized RIS (SORIS), is proposed. The architecture incorporates a microcontroller connected to a single-antenna receiver operating at the same frequency as the RIS unit elements, operating either in transmission or reflection mode. The transmitting RIS elements enable the low latency estimation of both the incoming and outcoming channels at the microcontroller's side. In addition, a machine learning approach for estimating the incoming and outcoming channels involving the remaining RIS elements operating in reflection mode is devised. Specifically, by appropriately selecting a small number of elements in transmission mode, and based on the channel reciprocity principle, the respective channel coefficients are first estimated, which are then fed to a low-complexity neural network that, leveraging spatial channel correlation over RIS elements, returns predictions of the channel coefficients referring to the rest of elements. In this way, the SORIS microcontroller acquires channel state information, and accordingly reconfigures the panel's metamaterials to assist data communication between a transmitter and a receiver, without the need for separate connections with them. Moreover, the impact of channel estimation on the proposed solution, and a detailed complexity analysis for the used model, as well as a wiring density and control signaling analysis, is performed. The feasibility and efficacy of the proposed self-organized RIS design and operation are verified by Monte Carlo simulations, providing useful guidelines on the selection of the RIS elements for operating in transmission mode for initial channel estimation.

Figures (12)

• Figure 1: The components of the proposed SORIS hardware architecture.
• Figure 2: The considered SORIS-aided wireless communication system.
• Figure 3: The communication frame of the proposed SORIS-empowered wireless system, including the estimation of the BS-SORIS (downlink) and SORIS-UE (uplink) channels.
• Figure 4: The designed ML-empowered channel prediction engine for our SORIS architecture.
• Figure 5: AMSE of the channel magnitude at different SORIS transmit elements.