The 6G-XCEL project has advanced the design and validation of the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework, a foundation for AI-native 6G networks. At its core is an AI Controller (AIC), a modular software system that unifies radio and optical domains, supports cross-controller communication, and enables end-to-end orchestration, monitoring, and self-repair.
The framework uses a distributed AIC architecture, allowing multiple algorithms to run in parallel and remain synchronized across different vendors and networks. Tests on experimental datasets provide initial indications of the benefits of AI-driven control and its potential for achieving faster and more reliable performance compared to conventional approaches.
Progress was made in security and trust aspects through a comprehensive threat analysis that identified risks such as data poisoning and privacy breaches so that they can be addressed. New modules were developed for data integrity, model validation, and privacy-preserving governance. The project also advanced novel AI techniques for use in the DMMAI context, including transfer learning, automated tuning, and explainability methods, making AI models more adaptable and transparent.
Work on energy efficiency introduced models to evaluate trade-offs between energy use, latency, and AI accuracy. Lightweight AI algorithms and optimized communication protocols were investigated to guide the design of sustainable AI-native networks
Key achievements include:
Demonstrating that AI-driven network control improves performance by 20–30%, with faster application response times and higher throughput compared to conventional approaches.
Developing mechanisms for synchronizing applications across multiple controllers, ensuring seamless service delivery.
Identifying security and privacy safeguards for distributed AI, including protections against data poisoning, backdoor models, and privacy breaches.
Advancing explainable AI techniques to build trust and transparency into network decision-making.
Establishing end to end network energy efficiency models that allow operators to optimize performance while minimizing energy consumption.
