In recent years, the complexity of 5G and beyond wireless networks has escalated, prompting a need for innovative frameworks to facilitate flexible management and efficient deployment. The concept of digital twins (DTs) has emerged as a solution to enable real-time monitoring, predictive configurations, and decision-making processes. While existing works primarily focus on leveraging DTs to optimize wireless networks, a detailed mapping methodology for creating virtual representations of network infrastructure and properties is still lacking. In this context, we introduce VH-Twin, a novel time-series data-driven framework that effectively maps wireless networks into digital reality. VH-Twin distinguishes itself through complementary vertical twinning (V-twinning) and horizontal twinning (H-twinning) stages, followed by a periodic clustering mechanism used to virtualize network regions based on their distinct geological and wireless characteristics. Specifically, V-twinning exploits distributed learning techniques to initialize a global twin model collaboratively from virtualized network clusters. H-twinning, on the other hand, is implemented with an asynchronous mapping scheme that dynamically updates twin models in response to network or environmental changes. Leveraging real-world wireless traffic data within a cellular wireless network, comprehensive experiments are conducted to verify that VH-Twin can effectively construct, deploy, and maintain network DTs. Parametric analysis also offers insights into how to strike a balance between twinning efficiency and model accuracy at scale.

翻译：近年来，5G及未来无线网络复杂性持续升级，迫切需要创新框架以实现灵活管理与高效部署。数字孪生（DTs）概念应运而生，为实时监控、预测性配置和决策过程提供了解决方案。现有工作主要聚焦于利用数字孪生优化无线网络，但仍缺乏构建网络基础设施与属性虚拟化表征的详细映射方法。在此背景下，我们提出VH-Twin——一种基于时序数据驱动的新型框架，可有效将无线网络映射为数字现实。VH-Twin通过互补的纵向孪生（V-twinning）与横向孪生（H-twinning）阶段实现差异化设计，并采用周期性聚类机制根据网络区域的地质与无线特征差异对其进行虚拟化。具体而言，纵向孪生利用分布式学习技术从虚拟化网络集群中协同初始化全局孪生模型；横向孪生则通过异步映射方案动态更新孪生模型，以响应网络或环境变化。基于蜂窝无线网络中的真实无线流量数据开展的综合实验表明，VH-Twin能够有效构建、部署并维护网络数字孪生。参数分析还揭示了在大规模场景下平衡孪生效率与模型精度的关键策略。