Beamforming Feedback-Based Line-of-Sight Identification Toward Firmware-Agnostic WiFi Sensing

This study realizes firmware-agnostic line-of-sight (LOS) identification to extend the range of WiFi-sensing applications. We developed a beamforming feedback (BFF)-based LOS identification algorithm. BFF frames are transmitted for multiple-input multiple-output (MIMO) communications. They can be obtained by capturing frames without custom firmware or specific chipsets and contain a beamforming feedback matrix (BFM) and subcarrier-averaged stream gain (SSG). These provide partial channel state information (CSI), and there are two major calculation steps involved from the CSI to the BFF: unquantized BFF (UQBFF) calculation and quantization. Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. Therefore, the proposed BFF-based method extracts features from the first-column vectors of the BFM. In addition, SSGs were leveraged to improve the accuracy. To demonstrate the feasibility of the proposed method, we conducted experiments using commodity off-the-shelf devices compliant with the IEEE 802.11ac standard. In the experimental evaluation, the proposed BFF-based method achieved an identification accuracy of 75.0%, whereas the CSI-based method achieved an accuracy of 81.2%. Accuracy comparisons revealed that the accuracy degradation of the BFF-based identification from the CSI-based identification was primarily caused by UQBFF calculations rather than quantization.