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Context-Aware Channel Sounder for AI-Assisted Radio-Frequency Channel Modeling
Published
Author(s)
Camillo Gentile, Jelena Senic, Anuraag Bodi, Samuel Berweger, Raied Caromi, Nada Golmie
Abstract
We describe a context-aware channel sounder that consists of three separate systems: a radio-frequency system to extract multipaths scattered from the surrounding environment in the 3D geometrical domain, a Lidar system to generate a point cloud of the environment in the same domain as the multipaths - providing a geometrical means to associate the multipaths to the environment scatterers - and a camera system to identify and classify the scatterers. With the assistance of artificial intelligence (AI), the data acquired by the three systems can be automatically reduced into a scatter-specific channel model. Two such applications are presented.
Proceedings Title
18th European Conference on Antennas and Propagation
Gentile, C.
, Senic, J.
, Bodi, A.
, Berweger, S.
, Caromi, R.
and Golmie, N.
(2024),
Context-Aware Channel Sounder for AI-Assisted Radio-Frequency Channel Modeling, 18th European Conference on Antennas and Propagation, Glasgow, GB, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956785
(Accessed October 1, 2025)