Ray entity based postprocessing of ray‐tracing data for continuous modeling of radio channel

Abstract

Ray-tracing data are usually given as angles of arrival and departure, transmitter and receiver coordinates, ray length and delay, received power level, and polarity. Usually, these values are given in raw data with some resolution that covers the area of interest where the simulation is performed. There are two main drawbacks of such approach: first, a huge amount of storage capacity is typically needed and second, although the area of interest is covered by a certain resolution, it is nearly impossible to interpolate between sample points and new time, and memory consuming simulations are necessary in order to increase the resolution of the simulations. This paper addresses the two mentioned drawbacks of ray tracing, suggesting a procedure based on the concept of ray entities both to enable continuous interpolation of ray-tracing data and reduce the memory needed for storing data. Ray entity is a set of rays that all undergo the same series of propagation phenomena (direct ray, diffraction, reflection, or scattering) on the same objects (building walls or edges). The method is given and illustrated for reflection and diffraction phenomena, and diffuse scattering was not included, but discussion is easily extended to this propagation type as well. The paper gives detailed statistics of entities' length and rays' count per simulated receiver point in few illustrative examples and provides an insight on how to interpolate angles of arrival and departure, ray length, and received power level in order to provide a continuous description of the radio environment.

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

“…Based on the results of the experiments, the authors managed to reduce the simulation time by a factor of more than 2. The article [19] proposed a method for post-processing ray-tracing data modeled in urban environments. It was proposed to combine rays that have undergone the same interaction into ray entities, which made it possible to reduce the load on memory by 11-12 times.…”

Accuracy Versus Complexity for mmWave Ray-Tracing: A Full Stack Perspective

“…Notice that both transmitterand receiver-dependent methods require the pre-processing to be performed whenever the reference node moves, thus leading to a significant overhead in dynamic environments. Other techniques speed up the RT's performance by either preemptively performing an exhaustive simulation to produce a coverage map for all the receivers' positions [45], or interpolating the metrics observed between a grid of simulated points [46]. Finally, [47] analyzes the performance of an RT as a function of the number of reflections, focusing only on the mean square error with respect to measurements in different indoor locations.…”