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

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 (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.