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arXiv 2602.16637eess.SP

主动RIS辅助MIMO系统用于生命体征提取:ISAC建模、深度学习和原型测量

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

De-Ming Chian, Chao-Kai Wen, Feng-Ji Chen, Yi-Jie Sun, Fu-Kang Wang

AI总结:

本文提出主动RIS辅助MIMO系统,通过深度学习和原型实验实现生命体征提取,提升呼吸检测可靠性并支持更高阶调制。

摘要:

我们提出了RIS-VSign系统,这是一种主动可重构智能表面(RIS)辅助的多输入多输出正交频分复用(MIMO-OFDM)框架,用于在集成感知与通信(ISAC)模型下提取生命体征。该系统分为两个阶段:RIS的相位选择器和呼吸率的提取。为减轻同步引起的共同相位漂移,差分莫比乌斯变换(DMT)被整合到深度学习框架中,命名为DMTNet,用于联合配置多个主动RIS元件。值得注意的是,训练数据是在仿真中生成的,而没有收集真实世界测量数据,所得到的相位选择器通过实验进行验证。在感知方面,多天线测量通过直流偏移校准和DeepMining-MMV处理,结合CA-CFAR检测和牛顿修正进行融合。原型实验表明,主动RIS部署提高了呼吸检测的可检测性,同时使更高阶调制成为可能;没有RIS时,呼吸检测不可靠,仅支持低阶调制。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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