ML-Assisted Beam Selection via Digital Twins for Time-Sensitive Industrial IoT

Abstract

In this article, we propose a machine learning (ML)-assisted beam selection framework that leverages the availability of digital twins to reduce beam training overheads and thus facilitate the efficient operation of time-sensitive IoT applications in dynamic industrial environments. Our approach employs a digital twin of the environment to create an accurate map-based channel model and train a beam predictor that narrows the beam search space to a set of candidate configurations. To verify the proposed concept, we perform shooting-and-bouncing ray (SBR) modeling for a reconstructed 3D model of an industrial vehicle calibrated using the real-world millimeter-wave (mmWave) propagation data collected during a measurement campaign. We confirm that lightweight ML models are capable of predicting the optimal beam configuration while enjoying considerably smaller size compared to the map-based channel model.