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PUBLICATIONS

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
Conference Dates
March 17-22, 2024
Conference Location
Glasgow, GB
Pub Type
Conferences

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Local Download
Wireless (RF), Voting systems, Virtual / augmented reality, Next generation networks, Networking, Network test and measurement, Network modeling and analysis, Mobile and wireless networking, Mobile, Machine learning, Internet of Things (IoT), Hardware for AI, Artificial intelligence, Applied AI, AI measurement and evaluation and Advanced communications

Citation

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 July 13, 2025)

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Created April 26, 2024, Updated July 13, 2025
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