Recent advancements in smart radio environment technologies aim to enhance wireless network performance through low-cost electromagnetic (EM) devices. Among these, metaprisms (MTPs)—a class of static, frequency-selective metasurfaces—stand out for their ability to create multiple beams at different frequencies without requiring channel state information (CSI) or active reconfiguration. Unlike reconfigurable intelligent surfaces (RIS), which rely on programmable elements and periodic tuning, MTPs operate passively, significantly reducing system complexity and overhead. The working principle of MTPs is specifically tailored for scenarios in which a large number of devices must be served simultaneously, each requiring low data rate and low latency, as envisioned by, e.g., Industrial Internet-of-Things (IoT). In this paper, we address the design of an ideal MTP by considering frequency-dependent reflection coefficients, and by identifying the general properties that are necessary to achieve the desired beam steering function in the angle-frequency domain. We also discuss the limitations of previous studies that employed oversimplified models, which may compromise performance. Key contributions include a detailed exploration of the equivalence of the MTP to an ideal S-parameter multiport (MP) model and an analysis of its implementation using Foster’s circuits. Additionally, we introduce a realistic MP network model that incorporates aspects overlooked by ideal scattering models, along with an ad-hoc optimization strategy for the obtained model. The performance of the proposed optimization approach and circuits implementation are validated through simulations using a commercial full-wave EM simulator, showcasing the effectiveness of the proposed method.

This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users’ signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.

Joint localization and communication services have received growing interest for the next 6G cellular scenarios. Therefore, this paper studies a multi-Reconfigurable Intelligent Surface (RIS) single-user Multiple Input Multiple Output (MIMO) scenario from a dual perspective. From one side, we analyze the capabilities of an Extended Kalman Filter (EKF) to track the trajectory of the user by processing the positioning information gathered by the signals backscattered from the RISs, whereas the RISs are in turn optimized accounting for the outcomes of the EKF estimator. On the other side, we analyze the impact of positioning onto the communication performance in terms of achievable rate. Numerical results have shown that it is possible to track moving nodes with high accuracy leveraging on, solely, the radio signal reflected by the RISs in the absence of a line-of-sight (LOS) path between the transmitter and the receiver ensuring, at the same time, good communications performance.