PHY3 – Reconfigurable Intelligent Surfaces
Thursday, 5 June 2025, 11:00-12:30, room 1.E
Session Chair: Christos Masouros (Univ. College London, UK)
On Performance of a RIS-Aided IoT Network with Direct Link and α – μ Fading
Deepak Kumar, Chandan Kumar Singh and Onel L. A. López (University of Oulu, Finland); Vimal Bhatia (Indian Institute of Technology Indore, India); Matti Latva-aho (University of Oulu, Finland)
This paper explores the performance of a reconfigurable intelligent surface (RIS)-aided Internet-of-Things (IoT) network under (\alpha-\mu) fading. The (\alpha-\mu) fading characterizes the nonlinearity of the propagation medium and encompasses negative exponential, one-sided Gaussian, Weibull, Rayleigh, and Nakagami-m fading models as special cases. We derive the cumulative distribution function of the effective channel (comprising the effect of the direct and RIS-assisted links) power gain to characterize the system performance. Specifically, we use this result to derive closed-form expressions of the outage probability (OP), symbol error rate under various digital modulation schemes, and sum throughput utilizing the Gaussian Chebyshev quadrature approximation. The asymptotic (high signal-to-noise ratio) OP is also derived, and the diversity order of the considered network is obtained. Monte-Carlo simulations are performed to verify the derived closed-form expressions. We show that deploying the RIS in close proximity to either the IoT source or the destination access point is preferred.
Human Activity Recognition with a Reconfigurable Intelligent Surface for Wi-Fi 6E
Nuno Paulino (INESC TEC, Portugal & University of Porto, Portugal); Mariana S Oliveira (University of Porto & INESC TEC, Portugal); Francisco M Ribeiro (INESC TEC, Portugal & University of Porto, Portugal); Luís Outeiro (INESC TEC, Portugal); Luis M. Pessoa (INESC TEC & Faculty of Engineering, University of Porto, Portugal)
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5 GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.
Reconfigurable Intelligent Surface Placement for Enhanced User Equipment Coverage
Sven Haesloop and Ehsan Tohidi (Fraunhofer HHI, Germany); Slawomir Stanczak (Technische Universität Berlin & Fraunhofer Heinrich Hertz Institute, Germany)
This paper addresses the coverage extension of User Equipment (UE) through the strategic placement of Reconfigurable Intelligent Surfaces (RISs). By considering Line-of-Sight (LOS) and surface orientation as key factors influencing RIS performance, we enhance existing models by incorporating 3D scenarios and more realistic wave propagation effects. Given that the RIS placement problem is NP-hard, we propose a low-complexity algorithm inspired by submodular optimization. Extensive numerical simulations in an urban environment demonstrate the scalability and effectiveness of the proposed approach, achieving coverage enhancements comparable to exhaustive search methods but with significantly reduced computational complexity. Furthermore, our results indicate that RIS placement effectively extends basic LOS coverage, though its primary benefit lies in coverage enhancement rather than delivering high data rates.
Experimental Approach to Adaptive RIS-Based Beam Tracking and User Localization in 5G mmWave Networks
Ayoub Mohammed Toubal, Timothee MacGarry, Rogerio Kaciava-Bombardelli, Ahmad Shokair and Youssef Nasser (Greenerwave, France); Julien de Rosny (CNRS, ESPCI Paris, PSL Research University, France); Geoffroy Lerosey (Greenerwave, France)
The utilization of Reconfigurable Intelligent Surfaces (RIS) offers a promising avenue for realizing electronic and software-driven beam control within wireless communication setups. In this paper, a beam management technique based on the user-controlled-RIS approach is presented, where the user feedback directs the operation of the RIS. This paper introduces a novel scheme for user equipment (UE) localization supported by RIS. The proposed approach focuses on accurately determining the UE’s position and dynamically adjusting the beam direction as the user moves. To achieve this, we assume that the UE can transmit signaling information, such as received signal strength (RSS), to the RIS via a low-frequency side link. In our design, the RIS leverages its ability to control the beamwidth, considering the number of its unit cells, thereby enhancing both positioning/tracking accuracy and communication efficiency. The proposed technique is implemented in a mmWave RIS-aided transmission system operating at 28 GHz. Measurement results showcase precise tracking capabilities, with angle mismatches ranging from 1◦to 2◦. The implemented algorithms facilitate flexi ble beam refinement through rapid beam management techniques, thereby minimizing tracking latency to near real-time levels. Fisher Information (FI) and Cram´ er-Rao lower bound (CRLB) analysis are derived to investigate the accuracy of the positioning and tracking system.
OTA Characterization of Reconfigurable Intelligent Surface at 5G mmWave Band
Ayoub Mohammed Toubal (Greenerwave, France); Fabian Tobias Bette (Rohde & Schwarz GmbH & Co. KG, Germany); Youssef Nasser (Greenerwave, France); Heinz Mellein (Rhode & Schwarz GmbH & Co. KG, Germany); Julien de Rosny (CNRS, ESPCI Paris, PSL Research University, France); Jean-Baptiste Gros, Vladislav Popov, Mikhail Odit, Vladimir Lenets and Geoffroy Lerosey (Greenerwave, France)
Recently, a new working item has been identified in pre-standardization activities (at ETSI) on reconfigurable intelligent surfaces (RIS) as a preparation for the the 3GPP 6G standardization. Among other topics, over-the-air (OTA) measurements are a common tool used to characterize the RIS. In this paper, we therefore propose OTA measurement techniques to evaluate RIS performance metrics and its compliance with 3GPP specifications through test and measurement (T&M) setups and equipments. These measurements are confirmed through extensive simulation results. The findings presented in this paper highlight the importance of OTA RIS measurements and show the impact of RIS technology on signal quality and network coverage, paving the way for the integration of this technology into next-generation wireless communication systems.
