A Modular Network Digital Twin for Radio Coverage Prediction: From Theory to Practice [* Cross-Reference *]

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Zaki-Hindi, Ayat; Sottet, Jean-Sébastien; Kumar, Sumit; TURCANU, Ion; Faye, Sébastien, 2025, "A Modular Network Digital Twin for Radio Coverage Prediction: From Theory to Practice [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.KKOMUR, LIST Open Repository, V1
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Description
Network Digital Twins (NDTs) offer a structured framework for modeling, predicting, and optimizing wireless networks. This paper presents a modular NDT implementation based on the GreyCat platform, integrating graph-based data models and external functional algorithms for indoor radio coverage prediction. For the first time, we implement an NDT system aligned with ITU-T Recommendation Y.3090, covering both basic and functional model instantiation from modular and interoperable abstract structures. We generated a practical dataset using a software-defined radio (SDR)-based OpenAirInterface5G setup, with a gNB and commercial UE deployed in a controlled environment. This real-world dataset was used to benchmark Gaussian Process Regression (GPR) and Convolutional Neural Network (CNN) models for predicting RSRP-based radio coverage. Our results show that CNN outperforms GPR in under-sampled conditions, and we demonstrate how the modular architecture supports flexible model integration and benchmarking. This work represents a significant step toward practical, data-driven NDT deployments for wireless systems. (2025-09-09)
***This entry has been automatically imported via Datacite by LIST harvest scripts. Please refer to https://doi.org/10.5281/zenodo.17086137 for the original and latest version of the dataset and data downloads*** (2025-09-11)
Subject Computer and Information Science
Keyword Network Digital Twins, 6G, software-defined radio
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