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How can the integration of AI in RAN help CSPs enhance network performance?

The integration of artificial intelligence (AI) in radio access networks (RAN) is set to transform network operations, driving enhanced performance, streamlined processes, and cost-effective deployment of advanced services like 5G. This forward-looking approach to AI adoption in RAN promises to elevate the capabilities of CSPs and set new standards for operational excellence.


AI is an inherent part of the radio networks and significantly benefits network operations, transforming them towards intent-driven networks. This shift involves AI models continuously adapting to meet user experience and energy efficiency targets, ultimately aiming to cost-effectively deliver superior customer experiences and support performance-based business models. This paper serves as a reference point for CSPs embarking on AI and automation journey.

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Main benefits of using AI in networks

Enhanced throughput

AI is used to predict and adjust radio link configurations based on channel conditions, traffic patterns, and user demands, ensuring high data rates for users with high bandwidth needs.

Improved handover speed and reduced dropped calls

AI anticipates the best target for handover and enables seamless call transfer between cells, minimizing disruptions and dropped calls caused by poor coverage, user movement, and network congestion.

Increased energy efficiency

AI predicts traffic patterns and cell usage to optimize energy consumption without compromising performance by activating or deactivating multiple-input, multiple-output (MIMO) functionalities in cells.

Network anomaly detection

AI continuously analyses network behaviour to identify unusual patterns, such as sleeping cells, allowing for early detection and corrective actions to maintain optimal network performance.

Further reading