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.

VH-Twin 是一个基于时间序列数据驱动的框架，通过竖直同构和水平同构阶段，以及基于地理和无线特性的网络区域的周期性聚类机制，有效地将无线网络映射到数字现实中。通过使用实际的无线网络流量数据，在细胞无线网络中进行综合实验，验证了 VH-Twin 构建、部署和维护网络数字孪生的有效性。参数分析还提供了在规模上在孪生效率和模型准确性之间取得平衡的见解。