AI-Powered Network Digital Twins: A Review of Intelligent Simulation, Prediction, Optimization, and Autonomous Network Control
Abstract
Network Digital Twins (NDTs) have emerged as a promising technology for creating virtual representations of physical communication networks and enabling network operators to analyze, predict, and optimize network behavior before applying changes to real infrastructure. The integration of Artificial Intelligence (AI) with digital twins can further enhance their ability to learn from network data, predict failures, optimize resources, and support autonomous network control. This review examines the state of research in AI-powered network digital twins, covering their architectures, data pipelines, modeling techniques, synchronization mechanisms, and AI components. Applications in traffic prediction, network planning, routing optimization, fault prediction, cybersecurity, network slicing, and resource management are analyzed. The review compares simulation-based, data-driven, and hybrid digital twin approaches and investigates the role of machine learning, reinforcement learning, graph neural networks, and generative AI. Major challenges—including real-time synchronization, data quality, computational cost, scalability, model fidelity, security, and interoperability—are critically discussed. The paper concludes by outlining future directions toward real-time, self-learning, and autonomous digital twins for 5G/6G and future AI-native networks.
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