Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning

TL;DR

VH-Twin addresses the challenge of mapping complex 5G-and-beyond wireless networks into digital twins by proposing a joint vertical and horizontal twinning framework with dynamic connectivity segmentation. It uses synchronous federated learning for an initial global twin across network clusters (V-twinning) and asynchronous updates to evolve the twin with changing conditions (H-twinning), forming region-based distributed twin networks (C-NDTs and G-NDT). The approach is validated on real-world wireless traffic data, showing competitive accuracy with notable gains in update efficiency and scalability, and revealing trade-offs between cluster granularity, BS density, and update frequency. The framework enables real-time monitoring, predictive configuration, and scalable digital replication of wireless networks, with practical impact for traffic forecasting and network management in urban environments.

Abstract

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.

Figures (5)

• Figure 1: Motivation and basic architecture of network DT enabled by joint vertical and horizontal learning.
• Figure 2: Framework of VH-Twin.
• Figure 3: Synchronous and asynchronous NDT mappings.
• Figure 4: Performance metrics of VH-Twin compared with single-level twinning.
• Figure 5: Impact of different values of $\psi$.