ultra-wideband (UWB) in practice. To this end, we propose UWB-Fi as a novel Wi-Fi sensing framework with ultra-wide bandwidth, leveraging only discrete and irregular channel samples. We first design a fast channel hopping scheme to enable arbitrary channel sampling across 4.7 GHz bandwidth on commodity Wi-Fi hardware without interrupting default communications. As no algorithm exists to exploit such channel samples, we establish a theoretical analysis driven by compressive sensing, so as to enable an explainable deep learning model. This model transforms sparse channel samples into high-dimensional (position) spectra, effectively avoiding the bias-variance dilemma in parameter estimation while encoding sufficient information for general sensing. Our extensive evaluations demonstrate that UWB-Fi successfully achieves centimeter-level fine-granularity multi-person sensing.">

Enabling Ultra-Wideband Wi-Fi Sensing via Sparse Channel Sampling (original) (raw)

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