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arXiv · 2603.23837

A Measurement-Calibrated AI-Assisted Digital Twin for Terahertz Wireless Data Centers

Mingjie Zhu·Yejian Lyu·Ziming Yu·Chong Han
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Abstract

Terahertz (THz) wireless communication has emerged as a promising solution for future data center interconnects; however, accurate channel characterization and system-level performance evaluation in complex indoor environments remain challenging. In this work, a measurement-calibrated AI-assisted digital twin (DT) framework is developed for THz wireless data centers by tightly integrating channel measurements, ray-tracing (RT), and implicit neural field (INF) modeling. Specifically, channel measurements are first conducted using a vector network analyzer at 300 GHz under both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. RT simulations performed on the Sionna platform capture the dominant multipath structures and show good consistency with measured results. Building upon measurement and RT data, an RT-conditioned INF is developed to construct a continuous radio-frequency (RF) field representation, enabling accurate prediction in RT-missing NLoS regions. The comprehensive RF map generated by DT can provide system-level analysis and decisions for wireless data centers.

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Mingjie Zhu, Yejian Lyu, Ziming Yu, Chong Han. 2026-03-25. A Measurement-Calibrated AI-Assisted Digital Twin for Terahertz Wireless Data Centers. https://arxiv.org/abs/2603.23837

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