Enhancing Intelligent 5G Networks with AI: From Edge to Cloud – Insights from 6GENABLERS-IA Project

The integration of Artificial Intelligence (AI) into 5G and emerging 6G networks is not just improving connectivity—it’s redefining what networks can achieve. From enabling cutting-edge applications like virtual reality (VR) and industrial IoT to creating more adaptive, intelligent systems, the 6GENABLERS-IA project is pioneering a new era of connected innovation. At the core of its approach is an architecture that seamlessly integrates Edge and Cloud technologies, optimized with AI-driven solutions.

The Need for Intelligent 5G/6G Networks

Traditional networks, while reliable, fall short in meeting the demands of modern applications that require ultra-low latency, dynamic scalability, and adaptive decision-making. Applications such as volumetric video processing for VR, IoT ecosystems, and industrial automation demand smarter networks that can:

• Predict and prevent performance bottlenecks.
• Dynamically optimize resource allocation.
• Support seamless user experiences across highly distributed environments.

The 6GENABLERS-IA project addresses these challenges by creating a framework where AI operates at every level, from real-time data collection at the Edge to advanced analytics in the Cloud.

Our Solution: Architecture Components

The 6GENABLERS-IA project delivers an advanced architecture that ensures networks are more intelligent, adaptive, and efficient. Here’s how it works:

1. Intelligent Telemetry

• Real-time telemetry systems continuously collect data from Edge devices and Cloud infrastructure to monitor Key Performance Indicators (KPIs) such as latency, CPU usage, and bandwidth.
• AI algorithms process this telemetry data to detect anomalies, predict potential issues, and optimize system performance proactively.
• This capability is essential for latency-sensitive applications like telemedicine, autonomous vehicles, and VR environments.

2. AI Training and Inference

• AI models are continuously trained and updated using real-world data stored in a Feature Store. This ensures that decision-making systems are always operating with the most accurate and up-to-date information.
• The MLOps (Machine Learning Operations) system streamlines the deployment and maintenance of these models, ensuring that they scale effortlessly as network demands grow.
• AI inference enables real-time decisions, such as dynamically adjusting network bandwidth to prioritize critical applications or users.

3. Experimental Infrastructure

• The project employs a hybrid Edge-Cloud infrastructure to test and validate advanced solutions like volumetric video processing for VR.
• By processing data locally at the Edge, the architecture reduces latency and enhances the quality of experience for users, particularly in immersive applications.
• This experimental approach ensures the scalability and reliability of solutions under real-world conditions.

4. Edge-Cloud Collaboration

• The architecture bridges the Edge and Cloud seamlessly, enabling distributed intelligence. AI at the Edge handles immediate, localized decision-making, while Cloud-based systems analyze broader trends and optimize system-wide operations.
• This collaboration reduces the computational load on the Cloud, improves response times, and enhances overall system efficiency.

Real-World Applications and Future Potential

The intelligent systems developed have far-reaching implications across industries:

• Immersive Virtual Reality: High-bandwidth, low-latency networks power seamless VR experiences for gaming, education, and training simulations. AI ensures that these systems adapt dynamically to user demands.
• Industrial IoT: Real-time telemetry and predictive analytics optimize machine performance, reduce downtime, and enhance operational efficiency in factories and supply chains.

Continuous progress in the field of AI and ML will enable 6G to reach its potential. Moving beyond intelligent networks towards autonomous systems, that proactively anticipate demands and adapt to them in real time, will suggest future directions to explore.

The 6GENABLERS-IA Legacy: Redefining Connectivity

The integration of AI into 5G/6G networks represents a significant leap toward intelligent, adaptive, and scalable connectivity. By bridging the Edge and Cloud with intelligent telemetry, advanced AI models, and scalable infrastructure, the 6GENABLERS-IA project contributes to shaping the future of network intelligence. These advancements are laying the foundation for transformative applications across industries, from immersive virtual environments to autonomous cities and smart factories.

Acknowledgements: The 6GENABLERS – AI for 6G, Ultra Automation and Optimization (TSI-063000-2021-10) is funded by the Spanish Ministry for Digital Transformation and Civil Service, and co-funded by the European Union – NextGenerationEU program via the Recovery and Resilience Facility (RRF).