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
  • 6G-XCEL at MWC 2024

    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 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 […]

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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​.