Sense with Polyface Mirror: Enhancing Wi-Fi Sensing Diversity via Programmable Metasurfaces

While gaining significant attention for device-free applications, Wi-Fi sensing still faces challenges in differentiating multiple targets; this stems from the design priorities of Wi-Fi systems that prioritize coverage and stability over sensing diversity. Existing proposals that either expand bandwidth or increase antennas to enhance sensing diversity can be confined by the limited access to Wi-Fi firm/hardware. To this end, we propose Mirror-Fi, a novel Wi-Fi sensing system that improves sensing diversity without modifying Wi-Fi firm/hardware. Exploiting the reconfigurability of metasurfaces, Mirror-Fi augments beamforming with spatially significant features, facilitating the construction of exclusive sensing signal links for individual targets. We innovate in an encoding scheme that equips each metasurface with a distinct phase coding sequence to mark link uniqueness. We then train a deep neural model to leverage prior coding sequences for decomposing non-linearly superimposed channel samples into mutually independent channels; it removes the need for complex channel matrix parameter estimation and mitigates hardware-related offsets inherent to Wi-Fi. Extensive evaluations demonstrate that, with a sufficient number of auto-configured metasurfaces, Mirror-Fi successfully achieves multi-target sensing.