A Survey on Digital Twin Networks: Architecture, Technologies, Applications, and Open Issues

Digital Twin (DT) technology represents a cutting-edge methodology that digitally maps physical entities with high fidelity, leading to the Digital Twin Network (DTN) through its integration with network technologies. DTN establishes bidirectional communication between virtual and physical spaces, enabling real-time monitoring, dynamic optimization, and precise control of physical networks. This addresses challenges posed by network expansion and service diversification, revolutionizing the management and optimization of complex network systems. Despite its potential, DTN implementation remains challenging, with research still nascent and lacking detailed guidelines. This paper aims to bridge this gap by presenting a comprehensive survey of the reference architecture for real-world DTN implementation and its key enabling technologies. It begins by defining the conceptual foundation of DTN and reviewing related architectural studies. This is followed by the proposal of a universal and scalable modular DTN architecture, encompassing the physical layer, data layer, DT model layer, and service layer. We then explore the critical enabling technologies required for implementing this architecture and analyze applications enhanced by DTN. Notably, We propose a five-level digital twin model evolution taxonomy framework that systematically reveals the evolution path from basic mapping to ultra-high-fidelity autonomous inference. This framework provides a structured evaluation benchmark for optimizing and advancing digital twin models. Finally, we discuss the primary open issues in DTN, offering theoretical and practical guidance for future research in this field.