arXiv AI
Jun 16

AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

arXiv:2511. 05522v4 Announce Type: replace-cross Abstract: Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications.

By Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia
arXiv Machine Learning
Sep 3

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

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.

By Mingjie Zhu, Ziming Yu, Guangjian Wang, Chong Han
arXiv Machine Learning
Sep 23

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...

By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)