802.11bf Multiband Passive Sensing: Reusing Wi-Fi Signaling for Sensing

2025-07-30

AI Summary

This study addresses the limited robustness of single-band Wi-Fi passive sensing. We propose a multi-band cooperative sensing framework aligned with the IEEE 802.11bf standard. Methodologically, we introduce MILAGRO—a novel end-to-end deep learning model that jointly leverages channel state information (CSI) from both sub-7 GHz and millimeter-wave bands for unified human presence detection, motion trajectory tracking, and activity recognition, while integrating a lightweight privacy-preserving mechanism. Experimental results across diverse indoor environments demonstrate presence detection and trajectory tracking accuracies of 95%–100%, significantly outperforming single-band baselines. Multi-band fusion markedly enhances interference resilience and cross-scenario generalizability. This work establishes a new paradigm for low-power, high-reliability, and privacy-aware Wi-Fi environmental sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents a novel multiband passive sensing system that leverages IEEE 802.11bf Wi-Fi signals for environmental sensing, focusing on both sub-7 GHz and millimeter-wave (mmWave) bands. By combining Channel State Information (CSI) from multiple bands, the system enhances accuracy and reliability in detecting human presence, movement, and activities in indoor environments. Utilizing a novel model, called MILAGRO, the system demonstrates robust performance across different scenarios, including monitoring human presence in workspaces and tracking movement in corridors. Experimental results show high accuracy (95-100%), with improved performance by integrating multiband data. The system also addresses key security concerns associated with passive sensing, proposing measures to mitigate potential risks. This work advances the use of Wi-Fi for passive sensing by reducing reliance on active sensing infrastructure and extending the capabilities of low-cost, non-intrusive environmental monitoring.

Problem

• Leveraging Wi-Fi signals for environmental sensing in multiple bands
• Enhancing human presence and movement detection accuracy indoors
• Addressing security concerns in passive sensing with mitigation measures

Innovation

• Multiband Wi-Fi signals for environmental sensing
• Combining CSI from multiple bands enhances accuracy
• MILAGRO model ensures robust performance