We present here a novel, fully discrete Ray Launching field prediction algorithm that takes advantage of environment preprocessing to efficiently trace rays undergoing both specular and diffuse interactions. The algorithm is “environment-driven” because rays are traced from the ray source according to the presence and distribution of obstacles in the surrounding space, therefore adapting ray density to the environment’s characteristics. The environment is discretized into simple regular shapes to facilitate faster geometric computations, to allow for visibility preprocessing and for the algorithm to be parallelized in a straightforward way. These innovative features combined together and implemented on a NVIDIA Graphical Processing Unit (GPU) are shown to speed-up computation by several orders of magnitude compared to more conventional algorithms, while retaining a similar accuracy level. The speed-up and prediction accuracy achieved in reference cases is presented in comparison to a pre-existing ray-based model and RF-coverage measurements.

A Discrete Environment-Driven GPU-Based Ray Launching Algorithm

E. M. Vitucci
Writing – Original Draft Preparation
;
V. Degli-Esposti
Writing – Original Draft Preparation
;
F. Fuschini
Writing – Review & Editing
;
M. Barbiroli
Writing – Review & Editing
;
2019

Abstract

We present here a novel, fully discrete Ray Launching field prediction algorithm that takes advantage of environment preprocessing to efficiently trace rays undergoing both specular and diffuse interactions. The algorithm is “environment-driven” because rays are traced from the ray source according to the presence and distribution of obstacles in the surrounding space, therefore adapting ray density to the environment’s characteristics. The environment is discretized into simple regular shapes to facilitate faster geometric computations, to allow for visibility preprocessing and for the algorithm to be parallelized in a straightforward way. These innovative features combined together and implemented on a NVIDIA Graphical Processing Unit (GPU) are shown to speed-up computation by several orders of magnitude compared to more conventional algorithms, while retaining a similar accuracy level. The speed-up and prediction accuracy achieved in reference cases is presented in comparison to a pre-existing ray-based model and RF-coverage measurements.
J. S. Lu; E. M. Vitucci; V. Degli-Esposti; F. Fuschini; M. Barbiroli; J. Blaha; H. L. Bertoni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/649226
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