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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SLICES-RI / CONVERGE Summer School – June 2025
Hands-On Introduction to Open RAN: Setting Up a 5G Network with Open Source Components by Merim Dzaferagic This week June 25–27, 2025, students in SLICES-RI / CONVERGE Summer School participated in hands-on workshop where they managed to: Goal was to give a practical, end-to-end perspective on Open RAN and 5G systems — not just theory, but actual […]
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EuCnC Demo on AI-based Zero Touch Management Systems in 6G
At the 2025 EuCNC & 6G Summit, a live video demo showcased the capabilities of the Zero-Touch Service Management (ZSM) framework developed by IMEC in the context of smart highway environments. The demo illustrated how ZSM autonomously manages network resources to ensure optimal performance for critical applications like traffic management, vehicle-to-everything (V2X) communication, and real-time […]
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IEEE International Conference on Communications 2025 – 6G-XCEL Papers Presentation
6G-XCEL team participated in : IEEE International Conference on Communications 2025 on 8–12 June 2025 in Montreal, Canada. The team presented two papers as described by TCD team below:
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EuCnC Demo on Cooperative Transport Interface over Open Source 7.2 split RAN and Virtualised Open PON Network
This demo is part of the 6GXCEL project and showcased the real-time integration of 5G New Radio (NR) and a virtualized Passive Optical Network (PON) system. An optimized scheduler is employed to reduce latency and improve resource allocation efficiency. The implementation leverages open-source and virtualization technologies to demonstrate convergence of optical and wireless communications. Thanks […]
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EuCnC &6G Summit 2025 Demo on Closed-loop intelligence using foundation models in ORAN Networks
Our 6G-XCEL team did a powerful demo at EuCnC &6G Summit 2025. The demo highlighted a real-time use case where the system collects real-time data from a live 5G O-RAN deployment and uses an LLM to make slice-level resource allocation decisions. While the demonstration is focused on a 5G/6G use case, the underlying framework, powered […]
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Japan NICT at the Connectivity section of Aalborg University (AAU)
On June 6, 2025, 6G-XCEL team from Aalborg University had a visit from the National Institute of Information and Communications Technology (NICT) https://www.nict.go.jp/en/ , which is Japan’s sole National Research and Development Agency specializing in the field of information and communications technology, at the Connectivity section of Aalborg University (AAU).Assistant Professor Shashi Raj Pandey, together with Prof. Petar Popovski and Dr. Junya Shiraishi, presented 6G-XCEL project to […]
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6G-XCEL at EuCNC & 6G Summit 2025
6-XCEL project participated in this year’s EuCNC & 6G Summit, one of Europe’s leading events for future communication technologies. This flagship conference brings together over 900 global delegates and more than 50 exhibitors to explore the future of communications — and we’re proud to be part of the conversation. 6G-XCEL is enabling resilient, energy-efficient, and high-capacity […]
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6G-XCEL featured at the IFIP Networking SLICES-RI Workshop on May 26, 2025
📡 Proud to see 6G-XCEL featured at the IFIP Networking SLICES-RI Workshop! 🇪🇺Our collaborative work on “Domain Adaptation and Transfer Learning applied to 5G datasets” — led by TCD, IBM, and AAU — was highlighted as a key example of experimentally-driven research and transatlantic cooperation.This is a great recognition of our joint efforts in building the foundations for trustworthy, AI-powered 6G networks! 🌍✨🗓️ We’re also excited […]
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Building DMMAI blocks: Onsite Collaboration with TCD and UoP
🚀 6G-XCEL Team joined forces onsite at Trinity College Dublin 🇪🇺🔬We’re thrilled to share that last week, our 6G-XCEL team with Merim Džaferagić and Georgios Tziavas have successfully set up an end-to-end 5G network as part of our collaborative mission to shape the future of 6G through Decentralized Multi-Party Multi-Network AI (DMMAI) Framework.✨ Key highlights:✅ Configured the E2 interface and exposed live KPM metrics✅ Exchanged hands-on experience across diverse hardware/software architectures✅ Defined next steps in our […]
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6G-XCEL at ICLR2025 Workshop
6G-XCEL Team presented in #ICLR2025 Workshop on Advances in Financial AI, where we presented our work on Distributed AutoML for Incremental Machine Learning Algorithms — a key outcome of our ongoing EU research project. Kudos to IBM Research team Seshu Tirupathi Dhaval Salwala! Read more on Linkedin post.
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.
Consortium
