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

Kunzhe Song, Maxime Zingraff, Huacheng Zeng

TL;DR

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 for sensing applications, without interfering with their primary communication functions is introduced.

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.

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

TL;DR

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 for sensing applications, without interfering with their primary communication functions is introduced.

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.
Paper Structure (26 sections, 23 equations, 13 figures, 2 tables)

This paper contains 26 sections, 23 equations, 13 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Radar-Inspired RF Hardware
  3. Hardware Design
  4. Baseband Signal Analysis
  5. Data Processing
  6. Signal Sanitization
  7. Differential Beamforming
  8. Human Activity Recognition
  9. Overview
  10. Structure Framework
  11. Loss Function
  12. Mask Segmentation
  13. Keypoint Estimation
  14. Experimental Evaluation
  15. Implementation
  16. ...and 11 more sections

Figures (13)

  • Figure 1: Architectural diagram of ARS.
  • Figure 2: Diagram of amplify-and-forward RF circuit.
  • Figure 3: Relationship between an object-moving distance and the signal phase rotation.
  • Figure 4: Signal constellation before (blue dot scatters) and after (a single red point) sanitization.
  • Figure 5: Observed signal phase and amplitude when a person moves toward the detector at a roughly constant speed.
  • ...and 8 more figures