AIRMap -- AI-Generated Radio Maps for Wireless Digital Twins

• URL: http://arxiv.org/abs/2511.05522v1
• Date: Tue, 28 Oct 2025 15:15:48 GMT
• Title: AIRMap -- AI-Generated Radio Maps for Wireless Digital Twins
• Abstract summary: We propose a deep-learning framework for ultra-fast radio-map estimation.<n>AirMap predicts path gain with under 5 dB RMSE in 4 ms per inference on an NVIDIA L40S.<n>A lightweight transfer learning calibration using just 20% of field measurements reduces the median error to approximately 10%.
• Score: 7.32219702732606
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and evaluated on 60,000 Boston-area samples, spanning coverage areas from 500 m to 3 km per side, AIRMap predicts path gain with under 5 dB RMSE in 4 ms per inference on an NVIDIA L40S -- over 7000x faster than GPU-accelerated ray tracing based radio maps. A lightweight transfer learning calibration using just 20% of field measurements reduces the median error to approximately 10%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

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