Tools, Models and Dataset for IEEE 802.11ay CSI-based Sensing
Steve Blandino, Tanguy Ropitault, Anirudha Sahoo, Nada T. Golmie
The ubiquitous deployment and availability of wireless communication devices create the perfect opportunity to provide new wireless local area network (WLAN) use-cases, by implementing new sensing capabilities, while leveraging existing communications equipment and signals. The availability of modeling tools and data-set is crucial to support the development of sensing techniques and to understand the end-to end performance of a joint wireless communication and sensing system, however most of the sensing performance evaluations are carried out using proprietary tools and data-set. In this paper we present an open source ray-tracing implementation enabling the evaluation of future WLAN sens scenarios. Moreover, we design a dataset consisting of more than 14 000 entries of millimeter wave channels and IEEE 802.11 ay signals to democratize the design of both data-driven and model driven communication and sensing algorithms. Thanks to these tools, we provide a first evaluation of a CSI based WLAN sensing system using IEEE 802.11 ay signals. The results indicate that existing communication systems can be used to enable sensing applications.
Wireless Communications and Networking Conference (WCNC), 2022 IEEE
, Ropitault, T.
, Sahoo, A.
and Golmie, N.
Tools, Models and Dataset for IEEE 802.11ay CSI-based Sensing, Wireless Communications and Networking Conference (WCNC), 2022 IEEE, Austin, TX, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933216
(Accessed March 1, 2024)