Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems
Abstract
With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.
Keywords
Cite
@article{arxiv.2506.01609,
title = {Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems},
author = {Dinh-Hieu Tran and Nazar Waheed and Yuris Mulya Saputra and Xingqin Lin and Cong T. Nguyen and Tedros Salih Abdu and Van Nhan Vo and Van-Quan Pham and Madyan Alsenwi and Abuzar Babikir Mohammad Adam and Symeon Chatzinotas and Eva Lagaunas and Hung Tran and Tu Ho Dac and Nguyen Van Huynh},
journal= {arXiv preprint arXiv:2506.01609},
year = {2025}
}
Comments
40 pages, 15 figures