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AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
arXiv – CS AI|Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia||4 views
🤖AI Summary
Researchers developed AIRMap, a deep-learning framework that generates radio maps for wireless network simulation over 100x faster than traditional ray tracing methods. The AI model achieves under 4 dB RMSE accuracy in 4 ms per inference and significantly outperforms traditional simulators when calibrated with field measurements.
Key Takeaways
- →AIRMap processes 2D elevation maps to predict wireless signal propagation over 100x faster than GPU-accelerated ray tracing.
- →The framework was trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments.
- →Lightweight calibration using 20% of field measurements reduces median error to approximately 5% versus 50% for traditional simulators.
- →The system enables real-time wireless network simulation and digital-twin applications with near-zero error in spectral efficiency.
- →Integration into Colosseum emulator and Sionna SYS platform demonstrates practical viability for wireless infrastructure modeling.
#ai#wireless#deep-learning#digital-twins#radio-maps#network-simulation#machine-learning#telecommunications
Read Original →via arXiv – CS AI
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