AZB Rectangle Shrinkage Method and Heterogeneous Computing Accelerated Full Image Theory Method Ray Tracing Enabling Complex and Massive Outdoor 6G Propagation Modeling
Yongwan Kim, Hyunjun Yang, Jungsuek Oh

TL;DR
This paper introduces a novel AZB rectangle shrinkage method combined with heterogeneous computing to significantly accelerate full-3D image theory ray tracing, enabling efficient modeling of complex, large-scale outdoor 6G propagation scenarios.
Contribution
The paper presents a new AZB rectangle shrinkage technique and a heterogeneous computing framework that greatly improves the efficiency and scalability of image theory ray tracing for massive outdoor environments.
Findings
Over 651 times faster than traditional solvers.
Capable of handling 1km x 1km dense urban scenarios.
Supports up to 6 ray bounces with thousands of observation points.
Abstract
Until now, despite their high accuracy, the utilization of the conventional image theory method ray tracers was limited to simple simulation environments with small number of field observation points and low maximum ray bouncing order due to their poor computational efficiency. This study presents a novel full-3D AZB rectangle shrinkage method and heterogeneous computing accelerated image theory method ray tracing framework for complex and massive outdoor propagation modeling. The proposed framework is divided into three parts: 1. Visibility preprocessing part. 2. Visibility tree generation part: in this part, a novel AZB rectangle shrinkage method that accelerates and reduces generation speed and size of visibility tree is proposed. 3. Shadow testing and field calculation part: in this part, a heterogeneous computing algorithm that can make possible to handle a large amount of field…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · UAV Applications and Optimization · Precipitation Measurement and Analysis
