Telecom networks are on the brink of a major transformation as operators experiment with AI-driven systems that can dynamically adjust traffic and service quality in real-time. The potential for AI to take on operational decision-making roles is becoming increasingly evident.
Recently, Nokia and AWS unveiled a cutting-edge network slicing system that leverages AI agents to continuously monitor network conditions and automatically optimize resources. This innovative setup is currently undergoing testing by leading telecom operators such as du in the United Arab Emirates and Orange in Europe and Africa, as announced jointly by Nokia.
The concept of network slicing allows operators to create virtual networks within a single physical infrastructure, each tailored to specific purposes. For instance, a network slice could be dedicated to emergency services or high-bandwidth consumer applications. While network slicing is a fundamental component of the 5G standard, traditional manual planning and fixed configurations have hindered the agility of networks in responding to fluctuating demands.
The newly introduced system aims to bridge this gap by introducing AI agents that analyze crucial network performance metrics like latency and congestion, while also taking into account external factors such as event schedules and weather conditions. These AI agents can dynamically adjust network settings to ensure services are consistently delivered at the agreed performance levels, as outlined in Nokia’s description of the pilot program.
The collaboration between Nokia and AWS merges Nokia’s slicing and automation tools with AI models delivered through Amazon Bedrock, AWS’s managed AI service platform. This unique approach is termed “agentic AI” by the companies involved.
The growing interest in such AI-driven systems underscores a persistent challenge faced by operators with the deployment of 5G networks. While 5G technology has significantly enhanced speed and latency, operators have struggled to monetize these technical advancements effectively. Network slicing is seen by many operators as a promising avenue for generating enterprise revenue, although the complexity of operations and uncertain demand have slowed its adoption.
The ability of networks to swiftly adapt to sudden spikes in demand, such as during events in crowded stadiums or emergencies requiring swift response, could enable operators to offer temporary connectivity or guaranteed service levels without requiring manual intervention.
Enterprises relying on private 5G networks for operations in factories or large venues stand to benefit from automated network adjustments, potentially influencing the design of applications that rely on consistent and predictable network performance.
The ongoing tests also shed light on the increasing involvement of cloud providers in telecom operations. Some operators have already migrated parts of their core networks to public cloud platforms or developed cloud-based control systems. Industry analysts have observed a rise in telecom cloud spending as operators modernize their networks and embrace software-driven infrastructure.
The integration of AI-driven control loops on cloud platforms represents the next evolutionary step, with AI systems monitoring conditions and implementing adjustments rapidly. While the technology is still in the testing phase, the experiments suggest that AI is evolving into an operational controller capable of dynamically managing physical and virtual resources in response to real-time events.
As AI continues to play a more prominent role in telecom operations, questions remain about deployment strategies, operator oversight of automated decisions, and regulatory considerations regarding AI control of critical communication infrastructure. Operators typically introduce automation gradually, maintaining human supervision to validate system behavior under real-world conditions, given the critical nature of telecom networks.
In conclusion, the convergence of AI and telecom networks holds immense potential for enhancing connectivity and service quality. By harnessing the power of AI to dynamically manage network resources, operators can meet the evolving needs of customers and businesses, paving the way for a more responsive, efficient, and reliable telecommunications ecosystem.
(Photo by M. Rennim)
Check out our related article: How e& is leveraging HR to integrate AI in enterprise operations
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