Digital-Twin-Assisted Clustering of Radio Frequency Multipath Components

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.