Active RIS-Assisted MIMO System for Vital Signs Extraction: ISAC Modeling, Deep Learning, and Prototype Measurements

We present the RIS-VSign system, an active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for vital signs extraction under an integrated sensing and communication (ISAC) model. The system consists of two stages: the phase selector of RIS and the extraction of respiration rate. To mitigate synchronization-induced common phase drifts, the difference of Möbius transformation (DMT) is integrated into the deep learning framework, named DMTNet, to jointly configure multiple active RIS elements. Notably, the training data are generated in simulation without collecting real-world measurements, and the resulting phase selector is validated experimentally. For sensing, multi-antenna measurements are fused by the DC-offset calibration and the DeepMining-MMV processing with CA-CFAR detection and Newton's refinements. Prototype experiments indicate that active RIS deployment improves respiration detectability while simultaneously enabling higher-order modulation; without RIS, respiration detection is unreliable and only lower-order modulation is supported.

Experimental Evaluation of Multiple Active RISs for 5G MIMO Commercial Networks

While numerous experimental studies have demonstrated the feasibility of reconfigurable intelligent surface (RIS) technology, most have primarily focused on extending coverage. In contrast, this paper presents an experimental evaluation of multiple active RISs deployed in a 5G multiple-input multiple-output (MIMO) commercial network, emphasizing enhancements in channel rank and throughput. We propose a low-complexity, codebook-based beamforming algorithm specifically tailored for multi-RIS configurations, which diversifies directional channels and reduces reliance on explicit channel state information. Field tests using a commercial base station and user equipment reveal that the multi-RIS system can improve channel rank and throughput by up to 14% compared to single-RIS deployments, while maintaining low computational complexity. These findings underscore the practical benefits of active multi-RIS systems for next-generation networks.

Active RIS-Assisted MIMO-OFDM System: Analyses and Prototype Measurements

In this study, we develop an active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) prototype compliant with the 5G New Radio standard at 3.5 GHz. The experimental results clearly indicate that active RIS plays a vital role in enhancing MIMO performance, surpassing passive RIS. Furthermore, when considering factors such as complexity, energy consumption, and performance, the comparative evaluation between passive RIS and active RIS reinforces the critical role of active RIS in MIMO systems. These findings underscore the practical significance of active RIS in improving MIMO gain in 5G scenarios.

A Novel Channel Model for Reconfigurable Intelligent Surfaces with Consideration of Polarization and Switch Impairments

Future wireless networks require the ability to actively adjust the wireless environment to meet strict performance indicators. Reconfigurable Intelligent Surface (RIS) technology is gaining attention for its advantages of low power consumption, cost-effectiveness, and ease of deployment. However, existing channel models for RIS often ignore important properties, such as the impairment in the RIS's switch component and the polarization efficiency among antennas, limiting their practical use. In this paper, we propose a new channel model for RIS that considers these ignored properties, including the reflected field, scattered field, and antenna resonant mode. We verify the proposed model through the practical implementation of a 4 x 4 RIS array with patch antennas in the 3.5 GHz band, using a phase shifter as the switch component of a RIS element. The equivalent model of the phase shifter is also formulated and incorporated into the channel model. We propose a blind controlling algorithm to discuss the properties of our channel model and emphasize the importance of considering polarization and tracking mechanisms for the controlling algorithm. Our channel model is an improvement over existing models and can be used in the practical design of RIS technology. The proposed algorithm provides a practical approach to controlling the wireless environment, suitable for various wireless applications.