FPNet: Joint Wi-Fi Beamforming Matrix Feedback and Anomaly-Aware Indoor Positioning
Ran Tao, Jiajia Guo, Yiming Cui, Xiangyi Li, Chao-Kai Wen, Shi Jin

TL;DR
FPNet is a deep learning framework that efficiently compresses Wi-Fi CSI feedback, provides high-accuracy indoor positioning, and reliably detects anomalies in dynamic environments, all using commodity Wi-Fi hardware.
Contribution
It introduces a unified approach combining feedback compression, precise positioning, and anomaly detection leveraging beamforming feedback matrices in Wi-Fi systems.
Findings
Achieves over 97% positioning accuracy with only 100 feedback bits.
Increases net throughput by up to 22.92%.
Detects out-of-distribution scenarios with over 99% accuracy.
Abstract
Channel State Information (CSI) provides a detailed description of the wireless channel and has been widely adopted for Wi-Fi sensing, particularly for high-precision indoor positioning. However, complete CSI is rarely available in real-world deployments due to hardware constraints and the high communication overhead required for feedback. Moreover, existing positioning models lack mechanisms to detect when users move outside their trained regions, leading to unreliable estimates in dynamic environments. In this paper, we present FPNet, a unified deep learning framework that jointly addresses channel feedback compression, accurate indoor positioning, and robust anomaly detection (AD). FPNet leverages the beamforming feedback matrix (BFM), a compressed CSI representation natively supported by IEEE 802.11ac/ax/be protocols, to minimize feedback overhead while preserving critical…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Wireless Networks and Protocols · Direction-of-Arrival Estimation Techniques
