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

2026-03-24

AI Summary

This work addresses the lack of high-fidelity channel modeling and system-level evaluation methods for terahertz (THz) wireless data centers in complex indoor environments. To this end, the authors propose a digital twin framework that synergistically integrates empirical measurements, ray tracing, and implicit neural fields—a first-time combination for THz channel characterization. Leveraging 300 GHz measurement data, ray tracing simulations via the Sionna platform, and implicit neural field modeling, the approach enables continuous and highly accurate reconstruction of radio frequency (RF) fields across both line-of-sight and non-line-of-sight regions. The resulting RF maps exhibit strong agreement with real-world measurements, substantially enhancing channel prediction accuracy and supporting more informed system-level decision-making in THz wireless data center deployments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Matrix & Tensor Methods
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Large-scale security measurements

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.

Problem

• Terahertz communication
• channel characterization
• wireless data centers
• system-level performance evaluation
• indoor environment

Innovation

• digital twin
• terahertz communication
• implicit neural field
• ray tracing
• channel measurement