Paper Info
Title
Beamforming Feedback-Based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Abstract
In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) 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 that does not require a pre-obtained database. This is in contrast with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, BFF-based MUSIC affords an alternative AoD estimation method without requiring access to the channel state information (CSI). Extensive experimental and numerical evaluations demonstrate that BFF-based MUSIC can successfully estimate the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study reveal that BFF-based MUSIC, where BFF is a highly compressed version of CSI in IEEE 802.11ac/ax, achieves an error of AoD estimation that is comparable to that of CSI-based MUSIC.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3180178
IEEE ACCESS
Keywords
DocType
Volume
Sensors, Multiple signal classification, Wireless fidelity, Estimation, Covariance matrices, Standards, Location awareness, Wireless sensing, channel state information, beamforming feedback, MUSIC algorithm
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Sohei Itahara102.03
Sota Kondo200.68
Kota Yamashita301.01
Takayuki Nishio410638.21
Koji Yamamoto513545.58
Yusuke Koda601.35