Radio sensing in the sub-10 GHz spectrum offers unique advantages over traditional vision-based systems, including the ability to see through occlusions and preserve user privacy. However, the limited availability of spectrum in this range presents significant challenges for deploying largescale radio sensing applications. In this paper, we introduce Ambient Radio Sensing (ARS), a novel Integrated Sensing and Communications (ISAC) approach that addresses spectrum scarcity by repurposing over-the-air radio signals from existing wireless systems (e.g., 5G and Wi-Fi) for sensing applications, without interfering with their primary communication functions. ARS operates as a standalone device that passively receives communication signals, amplifies them to illuminate surrounding objects, and captures the reflected signals using a self-mixing RF architecture to extract baseband features. This hardware innovation enables robust Doppler and angular feature extraction from ambient OFDM signals. To support downstream applications, we propose a cross-modal learning framework focusing on human activity recognition, featuring a streamlined training process that leverages an off-the-shelf vision model to supervise radio model training. We have developed a prototype of ARS and validated its effectiveness through extensive experiments using ambient 5G signals, demonstrating accurate human skeleton estimation and body mask segmentation applications.

翻译：亚10 GHz频谱的无线感知相较于传统视觉系统具有独特优势，包括穿透遮挡物能力和用户隐私保护特性。然而，该频段的频谱资源有限性对大规模无线感知应用部署构成了重大挑战。本文提出环境无线感知（ARS）——一种创新的通感一体化（ISAC）方法，通过复用现有无线系统（如5G和Wi-Fi）的空中无线电信号进行感知应用，在不干扰其主通信功能的前提下解决频谱短缺问题。ARS作为独立设备运行，被动接收通信信号，将其放大以照射周围物体，并采用自混频射频架构捕获反射信号以提取基带特征。该硬件创新实现了从环境OFDM信号中稳健提取多普勒与角度特征。为支撑下游应用，我们提出专注于人体活动识别的跨模态学习框架，其采用精简训练流程，利用现成视觉模型监督无线模型训练。我们已开发ARS原型系统，并通过环境5G信号的广泛实验验证其有效性，展示了精确的人体骨骼估计与体掩模分割应用。