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

TL;DR

This paper demonstrates experimentally that WiFi beamforming feedback can be used with a model-driven MUSIC algorithm to accurately estimate the angle of departure without needing pre-existing databases or channel state information.

Contribution

It introduces a novel BFF-based MUSIC method for AoD estimation that is model-driven, database-free, and performs comparably to CSI-based methods in WiFi sensing.

Findings

BFF-based MUSIC accurately estimates AoDs for multiple paths.

The method achieves error rates comparable to CSI-based MUSIC.

BFF provides a highly compressed alternative to CSI for AoD estimation.

Abstract

This study experimentally validated the possibility of angle of departure (AoD) estimation using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm, which does not require a pre-obtained database. This contrasts with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, the BFF-based MUSIC affords an alternative AoD estimation method without access to channel state information (CSI). Specifically, the extensive experimental and numerical evaluations demonstrated that the BFF-based MUSIC successfully estimates the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study revealed that the BFF-based MUSIC achieved a comparable error of AoD estimation to the CSI-based MUSIC, while BFF is a highly compressed version of CSI in IEEE 802.11ac/ax.