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6G Trans-Continental Edge Learning

Project description

The future of AI’s role in 6G networks

In the realm of communication technologies, integrating AI presents immense potential for advancement. However, challenges persist in its application, particularly in multi-network environments. With this in mind, the EU-funded 6G-XCEL project will pioneer research into multi-party AI controls, leveraging compute accelerators to synchronise radio and optical networks. This initiative aims to establish a comprehensive framework for AI in 6G, facilitating global adoption and standardisation while prioritising security and sustainability. By fostering collaboration among researchers and engaging with standardisation bodies, 6G-XCEL propels the development of decentralised AI-based network controls, setting the stage for transformative advancements in 6G technology.

Objective

Artificial Intelligence (AI) is widely studied and finding increasing adoption across communication technologies spanning network layers and business ecosystems. It is anticipated to play a central role in the design and operation of future 6G networks. Despite the promise of AI, there remain many obstacles to its use in communication networks. The introduction of software defined elements such as radio access network (RAN) intelligent controllers (RIC) enables multi-party applications for the control and management of networks. However, AI functions are still nascent and such structures do not extend to optical networks or multi-controller environments.
6G-XCEL seeks to address these challenges through research on high edge network use cases that employ multi-party AI controls running over compute accelerators to coordinate control across radio and optical networks. It will develop a reference framework for AI in 6G that will pave the way towards global validation, adoption and standardisation of AI approaches. This framework will enable decentralised AI-based network controls across network domains and physical layers, while promoting security and sustainable implementations. Using the latest AI algorithms and data compression, research on the resulting decentralised multi-party, multi-network AI (DMMAI) framework will enable the development of reference use cases, data and model repositories, curated training and evaluation data, as well as technologies for its use as a benchmarking platform for future AI/ML solutions for 6G networks.
6G-XCEL will bring together a large ecosystem of researchers from the EU and US to implement elements of the DMMAI framework in their testbeds and labs, integrating it into their research programs and validating the framework across platforms. Working with standardisation groups within each jurisdiction, 6G-XCEL will achieve joint progress towards large scale application of AI in 6G networks.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-JU-SNS-2023

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Coordinator

THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD, OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 454 330,00
Address
COLLEGE GREEN TRINITY COLLEGE
D02 CX56 Dublin
Ireland

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Region
Ireland Eastern and Midland Dublin
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 454 330,00

Participants (9)

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