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

2026-03-03

AI Summary

This work addresses the challenge of scarce spectrum resources in sub-10 GHz bands, which hinders large-scale deployment of radio-based sensing systems. To overcome this limitation, the authors propose Ambient Radio Sensing (ARS), a non-intrusive approach that leverages existing 5G and other communication signals for human activity perception without requiring dedicated spectrum or interfering with the original communication systems. The core innovation lies in a novel self-mixing RF architecture that robustly extracts Doppler and angular features from ambient OFDM signals. Furthermore, a cross-modal learning framework is introduced, utilizing off-the-shelf vision models to guide the training of the radio sensing model. Experimental results demonstrate that the prototype system achieves high-accuracy human skeletal estimation and body mask segmentation under real-world 5G signal conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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.

Problem

• spectrum shortage
• radio sensing
• sub-10 GHz
• human activity detection
• ambient signals

Innovation

• Ambient Radio Sensing
• Integrated Sensing and Communications
• Self-mixing RF architecture
• Cross-modal learning
• Human activity recognition