Nokia and AWS Join Forces to Revolutionize Real-Time 5G Network Slicing with AI Automation

Telecom networks are on the verge of a significant transformation, with operators exploring systems that leverage AI to dynamically adjust traffic and service quality in real time. The introduction of AI into operational decision-making processes is on the horizon.

In a recent development, Nokia and AWS unveiled a cutting-edge network slicing system that utilizes AI agents to continuously monitor network conditions and automatically optimize resources. This innovative solution is currently undergoing testing by leading telecom operators du in the United Arab Emirates and Orange in Europe and Africa, as announced jointly by Nokia.

The Era of Adaptive AI-driven Networks

Network slicing allows operators to create virtual networks within the same physical infrastructure, each tailored to specific purposes. For instance, a network slice can be customized for emergency services or high-bandwidth consumer traffic. While network slicing is a component of the 5G standard, traditional manual planning and fixed configurations have hindered networks’ ability to swiftly respond to changing demands.

The newly introduced system aims to bridge this gap by incorporating AI agents that monitor key network performance indicators such as latency and congestion, while also considering external data like event schedules and weather conditions. These AI agents can dynamically adjust network settings to maintain services at agreed-upon performance levels, as described by Nokia during the pilot phase.

AWS highlighted that the solution integrates Nokia’s slicing and automation tools with AI models delivered through Amazon Bedrock, their managed AI service platform. This collaborative approach is referred to as “agentic AI.”

The Promise of Autonomous Connectivity

The growing interest in such systems underscores a persistent challenge faced by operators. While 5G networks have delivered enhanced speed and reduced latency, monetizing these technical advancements has proven challenging. Many operators see network slicing as a potential revenue source from enterprise clients, although slow adoption has been attributed to operational complexities and uncertain demand.

By enabling networks to swiftly adapt to sudden surges in demand, such as in crowded stadiums or emergency scenarios, operators can potentially offer temporary connectivity or guaranteed service levels without manual intervention.

Orange has previously emphasized that enterprise customers expect connectivity to exhibit characteristics akin to cloud computing, where resources can scale on demand. Automated control of network resources through AI-driven systems could facilitate the transition of telecom services towards this model.

Convergence of Cloud Platforms and Telecom Network Operations

These tests also shed light on the increasing involvement of cloud providers in telecom operations. In recent years, certain operators have migrated segments of their core networks to public cloud platforms or constructed cloud-based control systems. Industry analysts at Dell’Oro Group have observed a rise in telecom cloud expenditure as operators modernize networks and embrace software-centric infrastructure.

The next phase involves layering AI-driven control loops atop cloud platforms, with AI systems monitoring conditions and effecting rapid adjustments. While the technology is still in the testing phase, Nokia’s announcement indicated that the collaboration with Orange involves demonstrations and pilot rollouts. Questions persist regarding deployment strategies, operator oversight of automated decisions, and regulatory perspectives on AI control of critical communication infrastructure.

Given the vital role of telecom networks in transmitting crucial data, reliability and accountability remain paramount concerns. Operators typically introduce automation gradually, maintaining human supervision to validate system behavior under real-world conditions.

The ongoing experiments signify the evolving role of AI as an operational controller, dynamically adjusting physical and virtual resources in response to real-time events.

(Image by M. Rennim)

Related Reading: Leveraging AI for Enhanced Enterprise Operations

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