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PRogrammable AI-Enabled DeterminIstiC neTworking for 6G

Project description

6G end-to-end solution for improved network reliability and efficiency

Fifth-generation (5G) network technology is undergoing an overhaul to adapt to future vertical use cases. For this endeavour to reach a viable place, a significant increase in 5G technology’s latency, security, reliability, energy and bandwidth capabilities would be necessary. The EU-funded PREDICT-6G project aims to develop an end-to-end solution for 6G that will allow it to reach a satisfying point across the path started by 5G. To do this, the project team will create an innovative multi-technology, multi-domain data plane, an AI-driven multi-stakeholder inter-domain control plane and a digital twin framework, which will complement each other to improve wired and wireless network capabilities in an aim to provide a deterministic 6G network solution.

Objective

6G is envisioned to accelerate the path started in 5G for catering to the needs of a wide variety of vertical use cases, both current and emerging. This will require major enhancements of the current 5G capabilities especially in terms of bandwidth, latency, reliability, security, and energy. PREDICT-6G’s mission is therefore set towards the development of an end-to-end 6G (E2E) solution including architecture and protocols that can guarantee seamless provisioning of services for vertical use cases requiring extremely tight timing and reliability constraints. To succeed, the solution will target determinism network infrastructures at large, including wired and wireless segments and their interconnections. PREDICT-6G will develop a novel Multi-technology Multi-domain Data-Plane (MDP) overhauling the reliability and time sensitiveness design features existing in current wired and wireless standards. The ambition is for the MDP design to be inherently deterministic. To achieve this, PREDICT-6G will develop an AI-driven Multi-stakeholder Inter-domain Control-Plane (AICP) for the provisioning of deterministic network paths to support time sensitive services as requested by end-customers and with different scaling ambitions, e.g. from the network in a single vehicle to a large, geographically dispersed network. This requires timely monitoring and prediction of the behavior of the complete network, including identifying potential sources of quality violations and analyzing various routes of the traffic flows. These capabilities will be delivered through the PREDICT-6G AI-powered Digital Twin (DT) framework, allowing the prediction of the behavior of the end-to-end network infrastructure, and enabling anticipative control and validation of the network provisions to meet the real-world time-sensitive and reliability requirements of the running services.

Coordinator

UNIVERSIDAD CARLOS III DE MADRID
Net EU contribution
€ 746 812,50
Address
CALLE MADRID 126
28903 Getafe (Madrid)
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 746 812,50

Participants (15)

Partners (1)