Accelerated ray tracing algorithm under urban macro cell

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.