DOAJ Open Access 2022

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

Sohei Itahara Sota Kondo Kota Yamashita Takayuki Nishio Koji Yamamoto +1 lainnya

Abstrak

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.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering

Penulis (6)

Sohei Itahara

Sota Kondo

Kota Yamashita

Takayuki Nishio

Koji Yamamoto

Yusuke Koda

Format Sitasi

APA MLA BibTeX

Itahara, S., Kondo, S., Yamashita, K., Nishio, T., Yamamoto, K., Koda, Y. (2022). Beamforming Feedback-Based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study. https://doi.org/10.1109/ACCESS.2022.3180178

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Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.1109/ACCESS.2022.3180178
Akses: Open Access ✓