AI and Digital Twins Enable Self-Optimizing Networks in New Validation

November 2, 2026 - 08:30

Samsung and KT have completed a validation of AI and digital twin technology designed to optimize networks automatically, removing the need for human intervention in routine traffic management. The two companies confirmed that the system improves traffic distribution across the network while maintaining stable performance.

The approach relies on a digital twin, a virtual replica of the physical network that mirrors real-world conditions in real time. AI models analyze data from this replica to predict congestion and identify the best configuration changes before they are applied to the live network. Because the twin can simulate outcomes safely, the system adjusts parameters on its own and verifies results without disrupting service for users.

During the validation, the automated system responded to changing traffic patterns faster than manual processes, redistributing load to prevent bottlenecks and keep capacity balanced. The companies reported measurable gains in efficiency and consistency, with fewer errors than conventional tuning methods that depend on engineer input.

The work points to a broader shift in telecom operations, where networks increasingly manage themselves through continuous sensing, simulation, and automated decision-making. Samsung and KT said the results support further development of autonomous network functions, with the goal of handling growing traffic demands and complex service environments without adding operational overhead.