A Measurement-Calibrated AI-Assisted Digital Twin for Terahertz Wireless Data Centers
Mingjie Zhu, Yejian Lyu, Ziming Yu, Chong Han

TL;DR
This paper introduces a measurement-calibrated AI-assisted digital twin framework for Terahertz wireless data centers, integrating measurements, ray-tracing, and neural modeling for accurate system simulation.
Contribution
It presents a novel digital twin approach that combines real measurements, ray-tracing, and neural fields to accurately model THz wireless channels in complex indoor environments.
Findings
RT simulations align well with measurements
INF model accurately predicts RF fields in NLoS regions
The digital twin enables effective system-level analysis
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Software-Defined Networks and 5G
