AIRMap -- AI-Generated Radio Maps for Wireless Digital Twins
This addresses the need for real-time, accurate wireless network simulation for digital-twin applications, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of computationally demanding channel modeling for wireless digital twins by proposing AIRMap, a deep-learning framework that predicts radio maps with under 5 dB RMSE in 4 ms per inference, over 7000x faster than GPU-accelerated ray tracing, and reduces median error to approximately 10% with lightweight calibration.
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.