Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
<|reference_start|>Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning: 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.<|reference_end|>
@article{zhang2024mapping,
    title={Mapping Wireless Networks into Digital Reality through Joint Vertical
  and Horizontal Learning},
    author={Zifan Zhang, Mingzhe Chen, Zhaohui Yang, Yuchen Liu},
    journal={arXiv preprint arXiv:2404.14497},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14497},
    primaryClass={cs.NI cs.LG eess.SP}
}