Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing
This addresses the challenge of implementing firmware-agnostic WiFi sensing for applications like indoor localization or activity recognition, but it is incremental as it builds on existing BFM-based sensing with a new estimation approach.
The paper tackles the problem of enabling WiFi sensing without direct access to channel state information (CSI) by proposing a method to estimate CSI from beamforming feedback matrices (BFMs), which are more practical to capture. The result shows that the estimated CSI matches ground-truth amplitude, and using multiple subcarriers improves accuracy compared to individual subcarrier estimation.
With regard to the implementation of WiFi sensing agnostic according to the availability of channel state information (CSI), we investigate the possibility of estimating a CSI matrix based on its compressed version, which is known as beamforming feedback matrix (BFM). Being different from the CSI matrix that is processed and discarded in physical layer components, the BFM can be captured using a medium-access-layer frame-capturing technique because this is exchanged among an access point (AP) and stations (STAs) over the air. This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs. However, the ability of BFM-based sensing has been evaluated in a few tasks, and more general insights into its performance should be provided. To fill this gap, we propose a CSI estimation method based on BFM, approximating the estimation function with a machine learning model. In addition, to improve the estimation accuracy, we leverage the inter-subcarrier dependency using the BFMs at multiple subcarriers in orthogonal frequency division multiplexing transmissions. Our simulation evaluation reveals that the estimated CSI matches the ground-truth amplitude. Moreover, compared to CSI estimation at each individual subcarrier, the effect of the BFMs at multiple subcarriers on the CSI estimation accuracy is validated.