RT Journal Article T1 Digital twins for next-generation mobile networks: Applications and solutions A1 Apostolakis, Nikolaos A1 Chatzieleftheriou, Livia Elena A1 Bega, DarĂ­o A1 Gramaglia, Marco A1 Banchs Roca, Albert AB Digital Twins (DTs) create fully-synchronized virtual representations of real-world systems, which can serve as interactive counterparts for artificial intelligence (AI) and machine learning (ML) algorithms, and hold significant importance for the upcoming 6G mobile networks. In this paper, we argue that DTs can improve all phases of the intelligent networks' workflow, due to their adaptability and scalability properties that would allow them to transparently integrate new AI/ML algorithms faster, more scalably, and more precisely. Our contribution is two-fold: first, we propose three specific application scenarios of DT-enhanced network architectures in the context of 6G. Second, using open-source tools, we implement and evaluate in detail one of them. Our results demonstrate that our DT reflects the characteristics of the physical object, successfully and scalably twinning it, and adapting to changing contextual conditions. PB IEEE SN 0163-6804 YR 2023 FD 2023-05-08 LK https://hdl.handle.net/10016/38982 UL https://hdl.handle.net/10016/38982 LA eng NO The work of University Carlos III of Madrid has been funded by the H2020 Project DAEMON (Grant Agreement No. 101017109), the Horizon Europe Project TrialsNet (Grant Agreement No. 101095871), and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D project 6G-CLARION. DS e-Archivo RD 17 jul. 2024