3D shooting and bouncing ray approach using an artificial intelligence-based acceleration technique for radio propagation prediction in indoor environments

Abstract

The shooting and bouncing ray approach is an important method used in ray-tracing simulations to estimate the propagation of radio waves. However, in this method, unnecessary rays, which are useless for follow-up and consume a large amount of simulation resources, may emerge. This study proposes an artificial intelligence-based acceleration solution, unlike geometry-based approaches in the literature. The proposed approach focuses on ray characteristics, not ambient geometry. An analysis of ray characteristics is carried out by trained simulation-decision engines at run-time. The decision engine training was carried out with datasets obtained from traditional shooting and bouncing simulations that use different parameters. The success of the proposed method was tested for three different real environments containing line-of-sight and non-line-of-sight receiver points, where the received signal strength measurements had been made earlier. For better training and more detailed analysis, a total of 63 simulations using different parameters were made in both training data and test simulations. The obtained results show the success of the proposed method.