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.

翻译：精确、低延迟的信道建模对于实时无线网络仿真与数字孪生应用至关重要。然而，传统建模方法（如射线追踪）计算量庞大，难以适应动态环境。本文提出AIRMap——一种用于超快无线电地图估计的深度学习框架，并配套构建了迄今规模最大的无线电地图数据集全自动流水线。AIRMap采用仅处理二维地形与建筑物高度高程图的单输入U-Net自编码器。基于120万个波士顿地区样本训练，并在四种不同城市与乡村环境（包含各异地形与建筑密度）中验证，AIRMap能够预测路径增益，在NVIDIA L40S上每次推理耗时4毫秒且均方根误差低于4 dB——相比基于GPU加速的射线追踪无线电地图提速超100倍。仅使用20%实地测量数据的轻量级校准可将中位误差降至约5%，显著优于误差超过50%的传统仿真器。将AIRMap集成至Colosseum仿真器与Sionna SYS平台后，其频谱效率与误块率相较于基于测量的信道几乎无误差。这些成果验证了AIRMap在无线数字孪生中实现可扩展、精确且实时无线电地图估计的潜力。