Point cloud data (PCD) can record a high-resolution model of the environment much more efficiently than the conventional geometrical mesh. This feature makes PCD suitable for the radio channel prediction of a new environment, especially for the millimeter-wave (mmWave). Since the multiple propagations and visibility computation using PCD are time consuming, progressive processing on PCD is introduced to make the prediction compatible with GPU-based frameworks such as OptiX and CUDA. Compared with the surface reconstruction from PCD, progressive data from the processing reserves more domain features such as diffraction wedge and the label of planar or nonplanar points. The numerical result of an outdoor environment shows that the proposed method has a close agreement with measurement, as well as two impressive speedups, 49.8 for the ray tracing and field calculation and 18.8 for the total prediction, compared with the original method using PCD.

中文翻译：

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基于 GPU 的点云渐进式处理无线电波传播预测


点云数据（PCD）可以比传统的几何网格更有效地记录环境的高分辨率模型。这一特性使得PCD适用于新环境的无线电信道预测，特别是毫米波（mmWave）。由于使用 PCD 的多重传播和可见性计算非常耗时，因此引入了 PCD 上的渐进式处理，以使预测与基于 GPU 的框架（例如 OptiX 和 CUDA）兼容。与PCD表面重建相比，处理中的渐进数据保留了更多的域特征，例如衍射楔和平面或非平面点的标签。室外环境的数值结果表明，与使用PCD的原始方法相比，所提出的方法与测量结果非常一致，并且有两次令人印象深刻的加速，光线追迹和场计算加速了49.8，总预测加速了18.8。