6G networks will need to model and optimise themselves in real time, and this project is building the world’s first open-access digital twin emulator to make that possible. 5G cannot keep up with emerging applications that demand terabit-per-second data rates, sub-millisecond response times, and centimetre-level location accuracy. Designing a 6G network that meets these requirements—while also using new technologies such as reconfigurable metamaterial transceivers and sub-terahertz spectrum—is an enormously complex task. Traditional modelling and optimisation methods are too slow and inflexible. TWIN6G addresses this gap by combining digital twins (virtual replicas of physical networks) with machine learning, so the network can self-optimise, self-configure, and self-heal in real time. If successful, the project will deliver an open-source emulator that integrates accurate physical models for emerging 6G technologies with physics-based machine learning. This could transform how telecommunications infrastructure is designed, tested, and operated—making networks faster, more energy-efficient, and more responsive without requiring constant human intervention. The staff-exchange programme also trains researchers across Europe, building a skilled workforce for next-generation communications.
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New emerging applications demand performance requirements that exceed the capabilities of 5G networks, requiring a rapid evolution towards 6G networks. 6G is expected to offer data rates of the order of Tbps, time responses of the order of submilliseconds, and localization accuracies of the order of sub-centimeter, while meeting united nations’ sustainable development goals. This calls for the deployment of paradigm shifting technologies and design methods, and the use of new frequency bands, including the deployment of reconfigurable metamaterial transceivers, the integration of communication and sensing functionalities, and the migration towards the sub-terahertz spectrum. This will make 6G an extremely complex system to model and optimize. Digital twins (DTs) and machine learning (ML) are two vital technologies to tackle the modeling and optimization complexity of 6G. A DT is a virtual replica of the 6G physical network. DTs are essential to model complex systems in real time, providing valuable insights into their behavior and performance, as well as for generating enormous amounts of training data. ML provides advanced analytics and decision-making capabilities, enabling 6G communication systems to self-optimize, self-configure, and self-heal. The integration of DTs and ML offers a powerful approach for modeling, simulating, and optimizing 6G communication networks. It is expected to lead to the creation of a highly intelligent and dynamic network environment, where physical and virtual objects interact seamlessly, and where decisions are made and executed in real time. TWIN6G is the first-of-its-kind staff exchange research and transfer-ofknowledge program whose aim is to build the world’s first open-access and open-source digital twin emulator to design 6G networks, integrating accurate physical models for emerging technologies and physics-based ML designs for dynamic and real-time network optimization.
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