用卫星图生成高程图，替代昂贵的激光雷达做无线环境建模。
Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
- 从卫星彩色图像直接学习高程图，无需3D点云数据。
- 相比纯图像方法，定位误差降低最高7.8%。
- 适合需要快速、低成本建模的6G网络规划场景。
下一代无线系统（如6G）工作在更高频段，信号传播对建筑物、植被等环境因素极为敏感。因此，精准的无线环境地图（REM）估计对网络规划与运行至关重要。现有方法（如射线追踪模拟器和深度学习生成模型）虽表现良好，但依赖激光雷达（LiDAR）生成的详细3D环境数据，这类数据获取成本高，每平方公里达数GB，且在动态环境中迅速过时。本文提出一种两阶段框架：第一阶段通过学习模型从卫星RGB图像直接预测高程图；第二阶段将高程图与天线参数输入REM估计器。在多个基于CNN的REM估计架构上，该方法相比仅使用图像的基线模型，RMSE最高降低7.8%，且推理时无需3D数据，为可扩展的无线环境建模提供实用替代方案。
原文摘要 · Abstract (English)
Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative models, achieve promising results but require detailed 3D environment data such as LiDAR-derived point clouds, which are costly to acquire, several gigabytes per km2 in size, and quickly outdated in dynamic environments. We propose a two-stage framework that eliminates the need for 3D data at inference time: in the first stage, a learned estimator predicts elevation maps directly from satellite RGB imagery, which are then fed alongside antenna parameters into the REM estimator in the second stage. Across existing CNN- based REM estimation architectures, the proposed approach improves RMSE by up to 7.8% over image-only baselines, while operating on the same input feature space and requiring no 3D data during inference, offering a practical alternative for scalable radio environment modelling.
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