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Electrical Engineering and Systems Science > Signal Processing

arXiv:2110.14211 (eess)
[Submitted on 27 Oct 2021 (v1), last revised 2 Feb 2022 (this version, v2)]

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

Authors:Sohei Itahara, Sota Kondo, Kota Yamashita, Takayuki Nishio, Koji Yamamoto, Yusuke Koda
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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.
Comments: Submitted to IEEE Access
Subjects: Signal Processing (eess.SP); Networking and Internet Architecture (cs.NI)
Cite as: arXiv:2110.14211 [eess.SP]
  (or arXiv:2110.14211v2 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2110.14211

Submission history

From: Sohei Itahara [view email]
[v1] Wed, 27 Oct 2021 06:51:57 UTC (302 KB)
[v2] Wed, 2 Feb 2022 19:04:26 UTC (3,700 KB)
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