EU-US 6G R&I Cooperation
6G Trans-Continental Edge Learning
This
About 6G-XCEL
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. DMMAI (Decentralized Multi-party, Multi-network AI) is a reference framework for AI in 6G that will pave the way towards global validation, adoption and standardization of AI approaches. This framework will enable the federation of AI-based network controls across network domains and physical layers, while promoting security and sustainable implementations. Research on the resulting decentralized multi-party, multi-network AI (DMMAI) framework will enable the development of reference use cases, data acquisition and generation methods, data and model repositories, curated training and evaluation data, as well as technologies and functionalities for its use as a benchmarking platform for future AI/ML solutions for 6G networks.
EU Testbeds
- OpenIreland Testbed
- Patras5G/P-NET Testbed
- SLICES RI
- City Lab, Smart Highway and 5GOpen, 5G-in-a-box portable and Time Sensitive Networks (TSN) Testbeds
US Testbeds
- CCI xG Testbed
- NSF Platforms for Advanced Wireless Research (PAWR) COSMOS
Use cases
- DMMAI for 6G spectrum management
- AI enhanced resource management
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Dr Dzaferagic as Co-Principal Investigator on the 6G-XCEL 6G SNS project
🎉 Congratulations to Dr Merim Džaferagić , a distinguished researcher at the CONNECT Centre, who has been appointed Assistant Professor in Computer Science within the Discipline of Networks and Distributed Systems at Trinity College Dublin. Dr Dzaferagic, who completed his PhD at Trinity College Dublin in 2020, brings a wealth of expertise in telecommunications engineering, […]
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Visit our booth at EuCNC & 6g summit
Visit Booth#67 at EuCNC & 6G Summit to learn about 6G-XCEL project and the use-cases it targets to promote AI in 6G networks: 1) AI enhanced resource management 2) 6G dynamic spectrum management
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6g XCEL at EuCNC & 6g summit
📢 EuCNC & 6G Summit Session 2 at Workshop 4 was about EU– US Cooperation, and our Professor Dan Kilper presented 6G-XCEL objectives at this well attended workshop. ❓ Curious to know what comes next? Visit our Booth#67 for a deep-dive with Merim Džaferagić and rest of research team!
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Workshop by University of Patras
“O-RAN on a decentralised multi-party, multi-network AI (DMMAI) framework” – Christos Tranoris Spyros Denazis https://lnkd.in/eXqq4sN8 DMMAI Framework is being designed and developed by 6G-XCEL partners, with target to provide tools for research and development of decentralized AI methods for network control extending across radio and optical networks and network domains (aka ‘multi-network’ approach). Building on […]
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6G-XCEL Project presentation
6G-XCEL Project presentation by Professor Dan Kilper from the Trinity College Dublin, Coordinator and TM – watch the full 9min video presentation presented as an SNS JU webinar. You can also check the LinkedIn post.
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6G-XCEL at MWC 2024
6G-XCEL was hosted and presented at Juniper Booth during Mobile World Congress 2024. Though in very early stages in the project, Juniper hosted a 6G-XCEL presentation at its AI Innovation demo station, explaining the project goals and presenting the DMMAI framework architecture and the consortium behind it.
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6G-XCEL Project kick-off!
6G-XCEL Project has kicked off in #dublinireland. This 36-month 6G #programme, co-funded by #SNSJU and the #EU, officially started on January 1st 2024. 10 EU Partners + 10 US Partners collaborating over the next 36 months.➡️ Partners discussed the project’s work plan, challenges & risks identified, while the implementation schedule was set. 📌 The goals are common:– 6G-XCEL is bringing together a large […]
Objective
1
Investigate and design a framework for decentralized, multi-party, multi-network AI for the control of 6G networks.
2
Determine achievable time scales for DMMAI in real time, near-RT, and non-RT control loops.
3
Develop efficient and scalable advanced AI methods for large scale time series data in decentralized multi-party, multi-network control.
4
Investigate methods to address the security and privacy of multi-party, multi-network AI network control for DMMAI in 6G.
5
Determine energy efficiency of DMMAI for 6G and methods for its study.
6
Create a flexible DMMAI framework that can be used with different AI orchestration platforms in the EU and US.
7
Establish a community of excellence in research on Networks & AI spanning the EU and US to provide foundation for its use in 6G.