As telecommunications networks become increasingly complex, the integration of advanced technologies such as network digital twins and generative artificial intelligence (AI) emerges as a pivotal solution to enhance network operations and resilience. This paper explores the synergy between network digital twins, which provide a dynamic virtual representation of physical networks, and generative AI, particularly focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We propose a novel architectural framework that incorporates these technologies to significantly improve predictive maintenance, network scenario simulation, and real-time data-driven decision-making. Through extensive simulations, we demonstrate how generative AI can enhance the accuracy and operational efficiency of network digital twins, effectively handling real-world complexities such as unpredictable traffic loads and network failures. The findings suggest that this integration not only boosts the capability of digital twins in scenario forecasting and anomaly detection but also facilitates a more adaptive and intelligent network management system.

翻译：随着电信网络日益复杂，网络数字孪生与生成式人工智能等先进技术的融合，已成为提升网络运维与韧性的关键解决方案。本文探讨了网络数字孪生（提供物理网络的动态虚拟表征）与生成式人工智能（尤其聚焦于生成对抗网络和变分自编码器）之间的协同作用。我们提出了一种融合这些技术的新型架构框架，旨在显著提升预测性维护、网络场景仿真及实时数据驱动决策能力。通过大量仿真实验，我们证明了生成式人工智能如何提高网络数字孪生的精度与运行效率，有效应对不可预测的流量负载和网络故障等现实复杂性。研究结果表明，该融合不仅增强了数字孪生在场景预测与异常检测方面的能力，还促进了更具适应性与智能化的网络管理系统的实现。