| Autor: |
Zeulin, Nikita, Ponomarenko-Timofeev, Aleksei, Galinina, Olga, Andreev, Sergey |
| Zdroj: |
IEEE Internet of Things Magazine; 2022, Vol. 5 Issue: 1 p36-40, 5p |
| Abstrakt: |
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 modeling for a reconstructed 3D model of an industrial vehicle calibrated using the real-world millimeter-wave propagation data collected during a measurement campaign. We confirm that lightweight ML models are capable of predicting the optimal beam configuration while enjoying a considerably smaller size compared to the map-based channel model. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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