Enabling Ultra-Wideband Wi-Fi Sensing via Sparse Channel Sampling

As a technology with ubiquitous presence in unlicensed spectrum, Wi-Fi has demonstrated prominent capabilities in both communication and sensing. However, since the bandwidth requirements for communication and sensing differ significantly, channel bandwidths excessive for communication (e.g., 160 MHz) still fail to achieve multi-person sensing. Though stitching multiple consecutive channels to expand the effective bandwidth sounds plausible, it may never reach 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.