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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Strengthening Transatlantic Research Collaborations in 6G
The 6G-XCEL project continues to foster deep academic exchange and innovation through international collaborations with leading U.S. institutions and researchers. 🎓 As part of our ongoing dialogue with the global research community, recent presentations showcased groundbreaking work from both EU and US partners: 1. Shashi Raj Pandey (AAU) presented “Push–Pull Coexistence towards 6G: From MAC Design to System […]
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Beyond 5G: Pioneering the Path to Energy-Efficient 6G Technologies by Prof. Dr.-Ing. Slawomir Stanczak
The 6G-XCEL consortium had the pleasure of hosting Prof. Dr.-Ing. Slawomir Stanczak, Professor at TU Berlin and Head of the Wireless Communications and Networks department at Fraunhofer HHI, for a powerful and forward-looking presentation. His talk, titled “Beyond 5G: Pioneering the Path to Energy-Efficient 6G Technologies”, explored the transformative potential of 6G RIC / xGRIC architectures and the crucial role of energy efficiency, AI-native design, and intelligent network control in […]
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i14y Lab Tour- 6G-XCEL exploring innovation
On Friday, July 11th, the 6G-XCEL consortium partners had the unique opportunity to visit i14y Lab—one of the first Open Testing & Integration Centers (OTICs) of the O-RAN Alliance.During the visit, we were guided through the lab’s impressive history, learned about its pioneering role in open RAN testing and certification, and toured both indoor and outdoor test facilities where cutting-edge O-RAN vendor equipment is validated.This visit reinforced […]
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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 […]
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
