AI-Enhanced Real-Time Wi-Fi Sensing Through Single Transceiver Pair

TL;DR

This paper introduces a real-time Wi-Fi sensing system using AI that leverages prior information and temporal correlation to improve accuracy with minimal hardware, demonstrated through human pose estimation and indoor localization.

Contribution

It provides a theoretical understanding of AI's role in Wi-Fi sensing and develops a practical system using a single transceiver pair for real-time perception.

Findings

AI enhances Wi-Fi sensing via prior info and temporal correlation

The system operates in real time on commodity hardware

Experimental results validate theoretical insights

Abstract

The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna count requirements. Remarkably, various AI-driven perception technologies have demonstrated the ability to surpass the traditional resolution limitations imposed by radar theory. However, the theoretical underpinnings of this phenomenon have not been thoroughly investigated in existing research. In this study, we found that under hardware-constrained conditions, the performance gains brought by AI to Wi-Fi sensing systems primarily originate from two aspects: prior information and temporal correlation. Prior information enables the AI to generate plausible details based on vague input, while temporal correlation helps reduce the upper bound of sensing error. Building on these insights, we developed a real-time, AI-based Wi-Fi sensing and visualization system using a single transceiver pair, and designed experiments focusing on human pose estimation and indoor localization. The system operates in real time on commodity hardware, and experimental results confirm our theoretical findings.