A Measurement-Calibrated AI-Assisted Digital Twin for Terahertz Wireless Data Centers
This work addresses the problem of complex indoor channel modeling for terahertz wireless data centers, which is incremental as it combines existing techniques like ray-tracing and neural fields with new measurements.
The authors tackled the challenge of accurate channel characterization and system-level performance evaluation for terahertz wireless data centers by developing a measurement-calibrated AI-assisted digital twin framework, which integrates channel measurements, ray-tracing, and implicit neural field modeling to generate comprehensive RF maps for analysis and decisions.
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.