Digital Twins for Next-Generation Mobile Networks: Applications and Solutions

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 article 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 with better precision. 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 the scenarios. Our results demonstrate that our DT reflects the characteristics of the physical object, successfully and scalably twinning it, and adapting it to changing contextual conditions.