Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection

TL;DR

This paper presents Ambient Radio Sensing (ARS), a novel approach that repurposes existing 5G signals for human activity detection, overcoming spectrum scarcity and enabling privacy-preserving, large-scale radio sensing.

Contribution

The paper introduces ARS, a hardware and learning framework that passively uses ambient 5G signals for sensing, with a cross-modal training approach leveraging vision models.

Findings

ARS accurately estimates human skeletons and body masks.

The prototype demonstrates effective sensing using ambient 5G signals.

ARS operates without interfering with primary communication functions.

Abstract

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.