KEYWORDS: Ray tracing, Radio propagation, 3D modeling, Computer simulations, Electromagnetism, Mobile communications, Diffraction, Antennas, Physics, Optoelectronics
A fast 3-D ray tracing propagation prediction model based on virtual source tree is presented in this paper, whose theoretical foundations are geometrical optics(GO) and the uniform theory of diffraction(UTD). In terms of typical single room indoor scene, taking the geometrical and electromagnetic information into account, some acceleration techniques are adopted to raise the efficiency of the ray tracing algorithm. The simulation results indicate that the runtime of the ray tracing algorithm will sharply increase when the number of the objects in the single room is large enough. Therefore, GPU acceleration technology is used to solve that problem. As is known to all, GPU is good at calculation operation rather than logical judgment, so that tens of thousands of threads in CUDA programs are able to calculate at the same time, in order to achieve massively parallel acceleration. Finally, a typical single room with several objects is simulated by using the serial ray tracing algorithm and the parallel one respectively. It can be found easily from the results that compared with the serial algorithm, the GPU-based one can achieve greater efficiency.
A novel ray tracing algorithm for high-speed propagation prediction in multi-room indoor environments is proposed in this paper, whose theoretical foundations are geometrical optics (GO) and the uniform theory of diffraction(UTD). Taking the geometrical and electromagnetic information of the complex indoor scene into account, some acceleration techniques are adopted to raise the efficiency of the ray tracing algorithm. The simulation results indicate that the runtime of the ray tracing algorithm will sharply increase when the number of the objects in multi-room buildings is large enough. Therefore, GPU acceleration technology is used to solve that problem. Finally, a typical multi-room indoor environment with several objects in each room is simulated by using the serial ray tracing algorithm and the parallel one respectively. It can be found easily from the results that compared with the serial algorithm, the GPU-based one can achieve greater efficiency.
In this study, an ray tracing propagation prediction model, which is based on creating a virtual source tree, is used because of their high efficiency and reliable prediction accuracy. In addition, several acceleration techniques are also adopted to improve the efficiency of ray-tracing-based prediction over large areas. However, in the process of employing the ray tracing method for coverage zone prediction, runtime is linearly proportional to the total number of prediction points, leading to large and sometimes prohibitive computation time requirements under complex geographical urban macrocell environments. In order to overcome this bottleneck, the compute unified device architecture (CUDA), which provides fine-grained data parallelism and thread parallelism, is implemented to accelerate the calculation. Taking full advantage of tens of thousands of threads in CUDA program, the decomposition of the coverage prediction problem is firstly conducted by partitioning the image tree and the visible prediction points to different sources. Then, we make every thread calculate the electromagnetic field of one propagation path and then collect these results. Comparing this parallel algorithm with the traditional sequential algorithm, it can be found that computational efficiency has been improved.
A three-dimensional (3D) ray-tracing model with the use of the uniform theory of diffraction and geometrical optics in which multiple reflections and diffractions are considered is presented in this paper to predict the channel coverage prediction of indoor environments, such as the received power and the RMS delay spread. The prediction model requires databases for the buildings and the objects layout in the indoor environment, in which the electrical characteristics (permittivity, conductivity, etc.) must be included. Electrical parameters directly influence the calculation of the reflection and diffraction phenomena. However, one cannot easily derive the electrical parameters due to the complexity of real building walls and the indoor objects. As a result, it is of great interest to quantify the prediction errors as functions of the electrical parameters inaccuracies. The Fresnel reflection coefficient and the diffraction coefficient are considered assuming several values in two different methods. The first method is to change the permittivity or the conductivity of the internal wall, the ceiling and the floor at the same time. The other method is to change the permittivity or the conductivity of the indoor objects while keep the electrical parameters of the internal wall, the ceiling and the floor unchanged. The mean error and standard deviation between predictions results of the original database and the error database are presented and analyzed. It is found that the accuracy of the model is sensitive to the relative permittivity and conductivity, and the sensitivity study also leads to the improvement of the accuracy of 3D ray-tracing model in indoor environments.
In this study, an improved ray tracing propagation prediction model, which is based on creating a new virtual source tree, is used because of their high efficiency and reliable prediction accuracy. In addition, several acceleration techniques are also adopted to improve the efficiency of coverage prediction over large areas. However, in the process of employing the ray tracing method for coverage zone prediction, runtime is linearly proportional to the total number of prediction points, leading to large and sometimes prohibitive computation time requirements under complex geographical environments. In order to overcome this bottleneck, the compute unified device architecture (CUDA), which provides fine-grained data parallelism and thread parallelism, is implemented to accelerate the calculation. Taking full advantage of tens of thousands of threads in CUDA program, the decomposition of the coverage prediction problem is firstly conducted by partitioning the image tree and the visible prediction points to different sources. Then, we make every thread calculate the electromagnetic field of one propagation path and then collect these results. Comparing this parallel algorithm with the traditional sequential algorithm, it can be found that computational efficiency has been improved dramatically.
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