Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study

Abstract

This paper proves that the angle of departure (AoD) estimation using the multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax, is possible. Although channel state information (CSI) enables model-driven AoD estimation, most BFF-based sensing techniques are data-driven because they only contain the right singular vectors of CSI and subcarrier-averaged stream gain. Specifically, we find that right singular vectors with a subcarrier-averaged stream gain of zero have the same role as the noise subspace vectors in the CSI-based MUSIC algorithm. Numerical evaluations confirm that the proposed BFFbased MUSIC successfully estimates the AoDs and gains for all propagation paths. Meanwhile, this result implies a potential privacy risk; a malicious sniffer can carry out AoD estimation only with unencrypted BFF frames.

Bi-Directional Beamforming Feedback-Based Firmware-Agnostic WiFi Sensing: An Empirical Study

“…First, if there exists channel reciprocity between the AP and STA, there is generally no BFM reciprocity (that is, V AP generally differs from V STA ), which is detailed in the following paragraph. (1-8) Second, although the AoD of the AP can be estimated from V AP [15], the sensing accuracy of the AoD of the AP using V STA and bi-directional BFMs (that is, using both V STA and V AP ) is not clear.…”