AI-Driven Traffic Engineering: Revolutionising Telecommunications Networks

Demand for high-speed, always-on connectivity is rising, which puts operators in the unenviable position of having to manage massive amounts of data. As more users stream videos, play online games and connect IoT devices, traditional traffic engineering methods struggle to keep up.

This is where Artificial Intelligence (AI) and machine learning (ML) come in,bringing predictive intelligence capability to telecoms networks. AI-driven traffic engineering transforms the network management process by automating the allocation and optimisation of resources, management of congestion, and traffic routing, ensuring users receive a seamless experience.

This article explores how AI is shaping the future of network traffic engineering, its real-world applications, the challenges of adoption and the future outlook.

AI-powered traffic engineering enhances network efficiency through data-driven automation. Unlike conventional methods that rely on pre-configured rules and human intervention, AI continuously learns and adapts to network conditions in real time.
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Real-Time Network Optimisation

AI-powered traffic engineering analyses network congestion, packet loss, and bandwidth usage to predict traffic patterns. By using algorithms that assess real-time conditions, AI can reroute data dynamically to avoid bottlenecks and ensure efficient delivery of services.

For instance, intent-based networking (IBN) leverages AI to interpret high-level business goals and automatically configure network traffic flows accordingly.
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Predictive Maintenance

AI not only optimises traffic but also predicts potential network failures. Traditional telecom networks react only after issues occur, leading to costly downtime and service disruptions. AI-powered predictive analytics allows telecom providers to:

• Detect hardware failures before they happen.
• Anticipate network congestion and take proactive measures.
• Automate infrastructure repairs or rerouting to minimse disruptions.

With AI-enabled predictive maintenance, telecom operators can prevent issues before they impact users—improving reliability and service quality.
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Dynamic Resource Allocation

AI facilitates adaptive resource allocation by dynamically distributing bandwidth, computing power, and storage based on real-time network demand. This capability is crucial for:

• 5G networks that require fast resource adjustments for ultra-low latency.
• IoT networks with millions of connected devices generating sporadic traffic bursts.
• Cloud computing infrastructures that balance load across multiple data centers.

By continuously learning from network conditions, AI ensures optimal resource utilisation, reducing waste and maximising efficiency.
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‍Intelligent Load Balancing

Load balancing prevents network overloads by distributing traffic across multiple servers and routes. AI-driven load balancing ensures:

• Smarter traffic routing based on past congestion patterns.
• Minimal packet loss by dynamically adjusting server workloads.
• Better redundancy, ensuring that no single node becomes a failure point.

For example, AI can analyse historical and real-time data to optimise routing decisions, automatically shifting network loads away from congested areas before users even experience slowdowns.

Leading telecommunications providers are actively integrating AI into their traffic engineering strategies.

T-Mobile US

• Partnered with Nvidia and OpenAI to develop AI-powered Radio Access Networks (RANs).
• Uses real-time AI-based monitoring to optimise cellular coverage and reduce dropped calls.

Microsoft’s Project Teal

• Uses Graph Neural Networks (GNNs) and Multi-Agent Reinforcement Learning (RL) to optimise traffic routing in wide-area networks (WANs).
• AI-driven traffic prediction models reduce bandwidth congestion, improving data center connectivity.

Rakuten Mobile

• Implements fully autonomous Open RAN networks using AI-powered self-optimising traffic management.
• Achieved 40% reduction in operational costs through automation.

Despite the advantages of AI-driven traffic engineering, several challenges must be addressed:

Data Privacy and Security

AI requires massive data collection to make intelligent routing decisions, raising concerns about privacy and cybersecurity. Operators must implement strict data protection measures while ensuring compliance with regulations like GDPR.

Integration with Legacy Networks

Many telecom networks still rely on decades-old infrastructure. Implementing AI-driven solutions requires upgrades and seamless integration with existing systems, which can be complex and costly.

Computational Demands

AI algorithms require significant processing power and cloud resources to analyze data in real-time. Edge computing solutions may help offload some AI computations, reducing latency and increasing efficiency.

As AI-driven traffic engineering continues to evolve, telecom providers will leverage more advanced techniques to improve network efficiency.

AI-Enabled 6G Networks

The transition from 5G to 6G will heavily rely on AI for self-learning traffic management, real-time spectrum allocation, and ultra-low latency applications.

AI-Augmented Intent-Based Networking

Future networks will self-adjust traffic flows automatically based on user demand, time of day, and application requirements—minimizing human intervention.

Federated Learning for Telecom AI

Federated Learning (FL) will enable AI models to be trained across multiple telecom networks without sharing raw data, enhancing privacy and security.