Abstract
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.
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