End-to-end Network digital TWIns for Native AI drivEn 6G architectures
6G research is already underway to shape the new generation telecommunication networks, aiming to deliver fully autonomous multi-technology, potentially 3D networking infrastructures. One of the key enablers of 6G is undoubtedly the Native integration of AI/ML at all system levels, components, and technology domains. However, this approach suffers in terms of decreased trustworthiness, which acts as a barrier to the adoption of data-driven technologies in future 6G networks. To this end, ENTWINE-6G research project aims to propose, design, and validate a novel automation architecture, aiming for the integration of Network Digital Twins (NDTs) into an AI-driven 6G System, to facilitate trustworthy automation of multi-technology 6G infrastructures. ENTWINE-6G proposes the cooperation of low-fidelity NDTs at the upper layers of the 6G architecture with high-fidelity twins that accurately model 6G infrastructure components. Moreover, ENTWINE-6G will contribute Trustworthy AI techniques, along with their associated enabling technologies and theoretical frameworks that will be leveraged to increase robustness and ensure SLA compliance. Finally, Generative techniques, including neural rendering, 3D diffusion, and NeRF, will emulate complex spatial and environmental conditions that will be validated with powerful digital twins tools that support Ray-Traced channels.
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Imported from OpenAIRE Spain | Grant 101312201.
Open sourceOverview
Grant agreement 101312201 | ENTWINE-6G
Open source6G research is already underway to shape the new generation telecommunication networks, aiming to deliver fully autonomous multi-technology, potentially 3D networking infrastructures. One of the key enablers of 6G is undoubtedly the Native integration of AI/ML at all system levels, components, and technology domains. However, this approach suffers in terms of decreased trustworthiness, which acts as a barrier to the adoption of data-driven technologies in future 6G networks. To this end, ENTWINE-6G research project aims to propose, design, and validate a novel automation architecture, aiming for the integration of Network Digital Twins (NDTs) into an AI-driven 6G System, to facilitate trustworthy automation of multi-technology 6G infrastructures. ENTWINE-6G proposes the cooperation of low-fidelity NDTs at the upper layers of the 6G architecture with high-fidelity twins that accurately model 6G infrastructure components. Moreover, ENTWINE-6G will contribute Trustworthy AI techniques, along with their associated enabling technologies and theoretical frameworks that will be leveraged to increase robustness and ensure SLA compliance. Finally, Generative techniques, including neural rendering, 3D diffusion, and NeRF, will emulate complex spatial and environmental conditions that will be validated with powerful digital twins tools that support Ray-Traced channels.