用AI快速生成无线信道地图，实时仿真更精准。
AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
- 仅需地形高程图，用U-Net模型快速预测信号衰减。
- 4毫秒内完成推断，误差低于4分贝，比传统方法快100倍。
- 适合无线数字孪生、网络仿真，尤其看重实时性的场景。
精确且低延迟的信道建模对实时无线网络仿真和数字孪生应用至关重要。传统方法如射线追踪计算成本高，难以应对动态环境。本文提出AIRMap，一种基于深度学习的超快速无线电地图估计框架，并构建了迄今最大的无线电地图数据集自动化生成流程。AIRMap采用单输入U-Net自编码器，仅需2D地形高程图（建筑高度）作为输入。在120万份波士顿区域样本上训练，并在四个不同城市与乡村环境中验证，涵盖多变地形与建筑密度。单次推理仅需4毫秒（NVIDIA L40S），路径增益预测均方根误差低于4分贝，速度超过基于GPU加速的射线追踪100倍。仅用20%实地测量数据进行轻量校准后，中位误差降至约5%，显著优于传统模拟器（误差超50%）。集成至Colosseum仿真器与Sionna SYS平台后，频谱效率与块错误率误差接近零，验证了其在无线数字孪生中实现可扩展、高精度、实时无线电地图估计的潜力。
原文摘要 · Abstract (English)
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.
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