FreeBFI: Enabling Fine-grained BFI Sensing with an Arbitrary Number of Antennas

WiFi sensing has garnered significant attention from both academic and industrial communities, largely due to the widespread deployment of WiFi infrastructure. However, most existing WiFi sensing works rely on Channel State Information (CSI), which can only be extracted from very few commercial WiFi devices. The widespread adoption of new WiFi protocols, such as IEEE 802.11ac and 802.11ax, presents a valuable opportunity to leverage the widely available Beamforming Feedback Information (BFI) for WiFi sensing. Several studies have explored the potential of BFI-based WiFi sensing. However, these works are limited to a specific number of antennas and cannot achieve fine-grained BFI sensing across an arbitrary number of antennas. In this work, we design and implement FreeBFI, the first BFI-based WiFi sensing system that can work with an arbitrary number of antennas. FreeBFI fully exploits the channel information and the SNR information contained in BFI to establish the relationship between BFI and target motion across arbitrary antenna counts. Furthermore, to extract fine-grained motion information from the established relationship, FreeBFI smartly fuses the information from multiple antennas and proposes a novel optimization algorithm to enhance the motion signal. To showcase the sensing capability of FreeBFI, we select two representative WiFi sensing applications: respiration monitoring and gesture recognition. We conduct comprehensive experiments covering a wide range of antenna counts and test the performance of FreeBFI on various WiFi devices. Experimental results demonstrate that FreeBFI not only delivers accurate and robust sensing performance under arbitrary antenna counts but also enhances sensing accuracy as all antennas are utilized. For respiration monitoring, FreeBFI significantly extends the sensing range from 4 m to 8 m. For gesture recognition, FreeBFI improves complex gesture recognition accuracy by over 20%. We believe this work marks a significant step toward the broader adoption of WiFi sensing on next-generation WiFi devices.