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PUBLICATIONS

Digital-Twin-Assisted Clustering of Radio-Frequency Multipath Components

Published

Author(s)

Anuraag Bodi, Jihoon Bang, Neeraj Varshney, Samuel Berweger, Chiehping Lai, Jelena Senic, Jack Chuang, Camillo Gentile

Abstract

Clustering radio-frequency (RF) multipath components (MPCs) fosters compact channel models by capturing the geometry of the scattering environment, yet "blind" methods based solely on RF data struggle to associate MPCs with individual scatterers. We address this by supplementing the RF channel sounder with camera and lidar systems that, together with AI-based algorithms, generate a segmented digital twin of the environment. By projecting MPCs onto this segmented twin, they are clustered directly according to the segments they fall on. We validate the framework on a robotic arm in a joint communications and sensing (JCAS) scenario, demonstrating reliable clustering by individual robot parts.
Citation
IEEE Antennas and Wireless Propagation Letters
Pub Type
Journals

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Keywords

Channel modeling, image segmentation, JCAS
Wireless (RF), Visualization research, Next generation networks, Networking, Network test and measurement, Network modeling and analysis, Mobile and wireless networking, Mobile, Internet of Things (IoT), Electromagnetics and Advanced communications

Citation

Bodi, A. , Bang, J. , Varshney, N. , Berweger, S. , Lai, C. , Senic, J. , Chuang, J. and Gentile, C. (2025), Digital-Twin-Assisted Clustering of Radio-Frequency Multipath Components, IEEE Antennas and Wireless Propagation Letters, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959654 (Accessed October 13, 2025)

Issues

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Created June 26, 2025, Updated July 12, 2025
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