AI-Enhanced Wi-Fi Sensing Through Single Transceiver Pair

2025-10-21

AI Summary

To overcome the fundamental radar-theoretic resolution limit of Wi-Fi sensing systems under hardware constraints, this paper proposes an AI-empowered fine-grained sensing framework using a single transceiver. Methodologically, we first identify and formalize two key mechanisms underlying AI’s performance gain—prior information modeling and temporal correlation exploitation—establishing a theoretical interpretability framework under hardware limitations. Integrating physics-informed feature extraction, temporal modeling via neural networks, and a lightweight end-to-end architecture, our approach enables pose estimation and indoor localization on a single RF chain. Experiments in real indoor environments achieve sub-decimeter localization accuracy (mean error < 0.85 dm) and 92.3% keypoint detection accuracy, reducing localization error by 47% compared to conventional methods. This work establishes a new paradigm for low-cost, high-accuracy, large-scale Wi-Fi sensing deployments.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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. We developed an AI-based Wi-Fi sensing system using a single transceiver pair and designed experiments focusing on human pose estimation and indoor localization to validate the theoretical claims. The results confirm the performance gains contributed by temporal correlation and prior information.

Problem

• AI enhances Wi-Fi sensing using prior information and temporal correlation
• System achieves real-time human pose estimation and indoor localization
• Operates with minimal hardware: single transceiver pair on commodity devices

Innovation

• AI leverages prior information for detail generation
• Temporal correlation reduces sensing error upper bound
• Real-time system uses single transceiver pair for sensing