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

TL;DR

This paper introduces the RIS-VSign system, combining active RIS, deep learning, and MIMO-OFDM for vital signs extraction, validated through prototype experiments showing improved respiration detection and higher modulation support.

Contribution

It proposes a novel active RIS-assisted MIMO-OFDM framework with a deep learning-based phase selector, validated experimentally, for enhanced vital signs sensing in ISAC systems.

Findings

Active RIS improves respiration detectability.

The system supports higher-order modulation.

Prototype experiments confirm system effectiveness.

Abstract

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 M\"obius 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.