{"ID":2849164,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05522","arxiv_id":"2511.05522","title":"AIRMap: AI-Generated Radio Maps for Wireless Digital Twins","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 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.","short_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...","url_abs":"https://arxiv.org/abs/2511.05522","url_pdf":"https://arxiv.org/pdf/2511.05522v3","authors":"[\"Ali Saeizadeh\",\"Miead Tehrani-Moayyed\",\"Davide Villa\",\"J. Gordon Beattie\",\"Pedram Johari\",\"Stefano Basagni\",\"Tommaso Melodia\"]","published":"2025-10-28T15:15:48Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\"]","methods":"[]","has_code":false}