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

Mingjie Zhu, Yejian Lyu, Ziming Yu, Chong Han

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.

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

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.
Paper Structure (13 sections, 15 equations, 6 figures, 1 table)

This paper contains 13 sections, 15 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Digital Twin Architecture
  3. Measurement-based Physical Twin
  4. AI-assisted Digital Twin
  5. Data Processing for DT
  6. Signal Model
  7. RT-conditioned Implicit Neural Field
  8. Interference and Coverage
  9. Performance Evaluation and Analysis
  10. Measurement Results of THz Data Center
  11. INF-based AI Twin Construction
  12. System-level Evaluation Example
  13. Conclusion

Figures (6)

  • Figure 1: Data center scenario scheme.
  • Figure 2: RT-conditioned implicit neural field.
  • Figure 3: MPC Twins. (a) Physical twin. (b) RT twin.
  • Figure 4: Received power map. (a) RT results. (b) prediction by the AI twin.
  • Figure 5: Path loss modeling of measurement, RT twin and AI Twin.
  • ...and 1 more figures