AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911475153502208 |
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| author | Saeizadeh, Ali Tehrani-Moayyed, Miead Villa, Davide Beattie Jr., J. Gordon Johari, Pedram Basagni, Stefano Melodia, Tommaso |
| author_facet | Saeizadeh, Ali Tehrani-Moayyed, Miead Villa, Davide Beattie Jr., J. Gordon Johari, Pedram Basagni, Stefano Melodia, Tommaso |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIRMap: AI-Generated Radio Maps for Wireless Digital Twins Saeizadeh, Ali Tehrani-Moayyed, Miead Villa, Davide Beattie Jr., J. Gordon Johari, Pedram Basagni, Stefano Melodia, Tommaso Signal Processing Artificial Intelligence 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. |
| title | AIRMap: AI-Generated Radio Maps for Wireless Digital Twins |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2511.05522 |