AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia

TL;DR
AIRMap is a deep-learning framework that rapidly generates accurate radio maps for wireless digital twins, significantly outperforming traditional methods in speed and accuracy, enabling real-time network simulation.
Contribution
The paper introduces AIRMap, a novel deep-learning approach with an automated dataset pipeline for ultra-fast, accurate radio map estimation using minimal input data.
Findings
Predicts path gain with under 4 dB RMSE in 4 ms per inference
Reduces median error to approximately 5% with minimal calibration
Achieves near-zero error in spectral efficiency and block-error rate in emulation environments
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…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · UAV Applications and Optimization · Indoor and Outdoor Localization Technologies
