Khalifa University and e& UAE have jointly unveiled a landmark white paper outlining the future of AI-native 6G networks. Titled “6G AI-Native Networks: Architecture, Intelligence, and the Path to Autonomous Connectivity,” the report presents a detailed framework to guide global discussions around IMT-2030 and upcoming 3GPP Release 21+ standards.
Importantly, the collaboration highlights the UAE’s growing role in shaping next-generation telecom infrastructure.
AI-Plane Architecture Redefines Network Intelligence
At the core of the blueprint is a new concept—an AI-plane integrated alongside traditional network layers. This approach allows networks to continuously learn, adapt, and make autonomous decisions.
Unlike 5G, which adds AI as an enhancement, the proposed 6G model embeds intelligence directly into the network’s architecture. As a result, networks can manage operations across radio, core, and edge environments in real time.
According to Marwan Bin Shakar, the goal is to transform networks into intelligent systems capable of reasoning and self-optimisation. He added that the framework introduces measurable AI KPIs, structured autonomy models, and open interfaces to support global standardisation.
Five Pillars Define AI-Native 6G Ecosystem
The white paper identifies five foundational pillars that will shape AI-native 6G networks:
- Pervasive AI and machine learning frameworks
- Distributed cloud-edge computing
- Advanced sensing and communication technologies
- Open and programmable network architectures
- Sustainability-driven system design
Together, these elements enable a shift towards cognitive infrastructure. Consequently, networks will support predictive optimisation, intent-based services, and fully autonomous operations.
Closed-Loop Autonomy and Digital Twins Take Centre Stage
A key highlight of the framework is closed-loop autonomy. This system enables networks to sense conditions, analyse data, and act instantly without human intervention. In addition, the architecture integrates digital twin ecosystems and distributed AI agents. These technologies allow real-time simulation, optimisation, and orchestration across multiple domains.
Ahmed Al Durra emphasised that such innovations require new governance models and lifecycle management for AI systems. He noted that the collaboration bridges academic research with real-world deployment needs.
Measurable KPIs to Guide Global Standards
To support standardisation, the paper introduces clear performance metrics. These include decision latency, learning accuracy, and energy efficiency per AI inference.Therefore, the framework not only offers a vision but also provides practical benchmarks for industry adoption and regulatory alignment.
Strengthening UAE’s Role in Global Connectivity Leadership
Through this initiative, e& UAE and Khalifa University aim to position the UAE as a global hub for AI-native connectivity research. Moreover, the partnership reflects a broader strategy to lead in telecom innovation, develop local talent, and contribute to international standards.
