AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
Researchers have developed AIRMap, a deep-learning framework designed for rapid radio-map estimation, which is crucial for real-time wireless network simulations and digital twins. The system utilizes a U-Net autoencoder that processes only terrain and building height data to predict path gain with high accuracy and speed. Trained on a large dataset from Boston and validated across diverse environments, AIRMap significantly outperforms traditional simulators, achieving under 4 dB RMSE in milliseconds per inference. AI
IMPACT Enables faster and more accurate wireless network simulations, potentially accelerating the development of real-time digital twins.