# Tri-Band Channel Measurement-Enabled Multi-Layer Digital Twin for Terahertz Wireless Data Centers

arXiv: https://arxiv.org/abs/2609.01699 · HTML: https://mlsanity.com/paper/2609.01699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021)
- [From Simulators to Digital Twins for Enabling Emerging Cellular Networks: A Tutorial and Survey](https://mlsanity.com/paper/2411.08907/) (2024)
- [Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis](https://mlsanity.com/paper/2111.04522/) (2021)
- [Prediction of Wireless Channel Statistics with Ray Tracing and Uncalibrated Digital Twin](https://mlsanity.com/paper/2411.13360/) (2024)
- [Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification](https://mlsanity.com/paper/2508.10372/) (2025)
- [Terahertz Wireless Data Center: Gaussian Beam or Airy Beam?](https://mlsanity.com/paper/2504.20410/) (2025)
- [When AI Meets Terahertz: A Survey on the Symbiosis of Artificial Intelligence and Terahertz Networks](https://mlsanity.com/paper/2604.22706/) (2026)

## Abstract (arXiv)

The rapid growth of AI computing has driven increasing demands for flexible and high-capacity data-center interconnections. Owing to its ultra-wide bandwidth and high spatial reuse capability, terahertz (THz) communication has emerged as a promising solution for future wireless data centers, while digital twins (DTs) enable efficient wireless planning and real-time optimization. In this work, a measurement-driven multi-layer DT framework is proposed for THz wireless data centers, where the physical, channel, evaluation, and manipulation layers are progressively constructed from bottom to top. First, extensive channel measurements are conducted at 140, 220, and 300 GHz to characterize frequency-dependent propagation behaviors. Based on the tri-band measurements, a measurement-calibrated physical twin is established by jointly optimizing the geometry, material, antenna, and hybrid propagation models. On top of the physical twin, a line-of-sight (LoS)-aware implicit neural field is developed to construct an AI channel twin for efficient channel reconstruction. The proposed AI twin learns location-dependent channel statistics from the calibrated twin, enabling real-time prediction of received power and LoS probability. Building upon the reconstructed channel field, a system-level evaluation layer is derived to analyze coverage and interference for both AP-to-rack and rack-to-rack communications. Experimental results show that the proposed AI twin achieves lower power reconstruction error than existing neural-field baselines while maintaining real-time inference capability. Moreover, the ceiling-mounted AP deployment achieves over 90% coverage under a 10 dB signal-to-interference-plus-noise ratio (SINR) threshold, demonstrating the effectiveness of the proposed DT framework for THz wireless data-center planning and optimization.