[2511.05522] AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

Electrical Engineering and Systems Science > Signal Processing arXiv:2511.05522 (eess) [Submitted on 28 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v3)] Title:AIRMap: AI-Generated Radio Maps for Wireless Digital Twins Authors:Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia View a PDF of the paper titled AIRMap: AI-Generated Radio Maps for Wireless Digital Twins, by Ali Saeizadeh and 6 other authors View PDF HTML (experimental) Abstract:Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly o...