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arXiv Machine Learning By Mingjie Zhu, Ziming Yu, Guangjian Wang, Chong Han

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

Read the original on arXiv Machine Learning →

The paper proposes a measurement-driven multi-layer digital twin framework for terahertz (THz) wireless data centers. It begins with extensive tri-band channel measurements at 140, 220, and 300 GHz to calibrate a physical twin that optimizes geometry, material, antenna, and propagation models. An AI channel twin, built on a line-of-sight aware implicit neural field, learns location-dependent channel statistics to enable real‑time prediction of received power and LoS probability, which feeds into a system‑level evaluation layer that analyzes coverage and interference for AP‑to‑rack and rack‑to‑rack links. Experimental results show the AI twin achieves lower power reconstruction error than existing neural‑field baselines while maintaining real‑time inference, and ceiling‑mounted AP deployment yields over 90% coverage at a 10 dB SINR threshold.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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