  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.
