Generative AI for Network Optimisation: The Next Evolution of Self-Optimising Telecoms

Telecoms networks are growing in complexity as data traffic from 5G, IoT and AI-driven applications continues to rise. Traditional network optimisation techniques are struggling to keep pace with real-time traffic fluctuations and require constant human intervention. Generative AI introduces self-optimising, adaptive networks capable of real-time adjustments, reducing the need for manual intervention. .

Generative AI models, particularly deep reinforcement learning (DRL) and generative adversarial networks (GANs), are reshaping telecoms infrastructure. From predicting traffic congestion to self-configuring network parameters, generative AI provides zero-touch automation in network operations.

This article outlines how generative AI is transforming telecoms networks, and explores the latest applications, real-world case studies, and future challenges.

Generative AI is revolutionising network traffic management, resource allocation and network security. Unlike conventional machine learning models used for pattern recognition, generative AI can proactively create, simulate and optimise network conditions.

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1. AI-Powered Predictive Network Traffic Forecasting

Traditional network traffic forecasting relies on historical data and rule-based algorithms, which often fail under unpredictable conditions. Generative AI enables real-time, adaptive forecasting which supports  telecoms operators in several ways:

• Anticipating congestion patterns hours or even days in advance;
• Dynamic adjustment of bandwidth allocation to cater to areas of high demand;
• Reduced latency in high-load environments, which improves the  user experience.

Techniques such as Recurrent Neural Networks (RNNs) and Transformer-based AI models are being leveraged to provide hyper-accurate demand prediction. Transformer-based models are superior due to their superior architecture and efficiency, including improved accuracy (which can highlight any dependencies in network traffic data), parallel processing and self-attention capability, and scalability.

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2. Reinforcement Learning for Dynamic Resource Allocation

Deep reinforcement earning (DRL) enables telecoms networks to allocate resources dynamically without the need for human input. DRL agents are able to learn from continuous feedback on network performance, which benefits the network in several ways:

• Spectrum management can be automated based on real-time demand;
• Energy consumption is optimised by adjusting network power usage;
• Quality of service (QoS) can be improved through network parameters that are self-adjusting network parameters.

Leading telecoms providers are integrating DRL models with software-defined networking (SDN) to create fully adaptive networks that can react to changing conditions in milliseconds.

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3. Multi-Agent Generative AI for Network Slicing

Network slicing allows telecoms providers to create multiple virtual networks over the same physical infrastructure, each tailored to specific use cases (such as IoT, cloud gaming and remote surgery). Multi-Agent Generative AI is being used for other purposes, including:

• Autonomous optimisation of  network slices, ensuring efficient bandwidth distribution;
• Balancing latency-sensitive applications such as augmented reality (AR) and ultra-reliable low-latency communications (URLLC);
• Reducing operational costs by automating network provisioning.

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4. AI-Driven Self-Healing Networks

Generative AI is enabling networks to detect and resolve failures before they affect service. AI-driven anomaly detection has several advantages, including:

• Identifying network disruptions in real-time and predicting failures;
• Triggering automated self-healing mechanisms to reroute traffic or deploy fixes;
• Enhanced cybersecurity capability, through the detection of AI-driven cyber threats before they occur.

BT Group – AI-Optimised Network Maintenance

BT is leveraging AI-based predictive maintenance models to forecast equipment failures, reducing operational downtime. By integrating GANs with network analytics, BT has reduced maintenance costs while improving service reliability.

Deutsche Telekom – Self-Configuring Networks

Deutsche Telekom is developing AI-powered self-configuring networks using DRL algorithms. These networks autonomously adjust parameters to optimise coverage and bandwidth allocation without human intervention.

Rakuten Mobile – Cloud-Native AI-Driven Networks

Rakuten Mobile is pioneering AI-native cloud networks, using generative AI to automate network slicing and engage in dynamic spectrum allocation, improving efficiency across its 5G network.

While generative AI offers significant potential, several challenges must be addressed:

1. High Computational Demands

Training generative AI models requires massive computing power and energy consumption. As such, edgeAI solutions are being explored to reduce computational loads on centralised servers.

2. Security Risks and AI-Powered Cyber Threats

Generative AI can be exploited to generate attacks on telecoms networks. To counteract these emerging threats, AI-driven security frameworks must be put in place.

3. Ethical and Regulatory Concerns

AI-driven network automation raises concerns about data privacy, accountability and algorithmic bias. To mitigate these concerns, many governments are introducing AI governance policies to ensure responsible deployment of AI in telecoms.

As AI continues to evolve, generative models will become an integral part of autonomous telecoms infrastructure. Key advancements include:

• Federated learning for AI-driven telecoms security, enabling secure, decentralised AI training without sharing sensitive network data;
• Quantum AI-powered optimisation, allowing operators to leverage quantum computing for complex decision-making.
• AI-native network architectures, where AI is embedded directly into the network, such that infrastructure is fully self-learning.