An Improved Ray Tracing Acceleration Algorithm Based on Bounding Volume Hierarchies

Ray tracing is an efficient channel modeling method. However, the traditional ray tracing method has high computation complexity. To solve this problem, an improved bounding volume hierarchies (BVH) algorithm is proposed in this paper. Based on surface area heuristic (SAH) and spatial distance, the proposed algorithm can effectively reduce the number of unnecessary intersection tests between ray and triangular facets. In addition, the algorithm fully considers the influence of ray action range, which can not only make up for the defects of spatial division based on uniform grid method and k-dimensional (KD) tree, but also solve the problem of unsatisfactory spatial division based on traditional BVH algorithm. The simulation results show that compared with the traditional BVH algorithm, the proposed algorithm can improve the computation efficiency by 20% to 35% while ensuring the computation accuracy.

An SBR Based Ray Tracing Channel Modeling Method for THz and Massive MIMO Communications

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology have been proved significant for the sixth generation (6G) communication systems, and have gained global interests. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with acceleration technology to model THz and massive MIMO channel. The results of ray tracing (RT) simulation in this paper, i.e., angle of departure (AoD), angle of arrival (AoA), and power delay profile (PDP) under the frequency band supported by the commercial RT software Wireless Insite (WI) are in agreement with those produced by WI. Based on the Kirchhoff scattering effect on material surfaces and atmospheric absorption loss showing at THz frequency band, the modified propagation models of Fresnel reflection coefficients and free-space attenuation are consistent with the measured results. For massive MIMO, the channel capacity and the stochastic power distribution are analyzed. The results indicate the applicability of SBR method for building deterministic models of THz and massive MIMO channels with extensive functions and acceptable accuracy.

A 3D Non-Stationary Geometry-Based Stochastic Model for Industrial Automation Wireless Communication Systems

Industrial automation is one of the key application scenarios of the fifth (5G) wireless communication network. The high requirements of industrial communication systems for latency and reliability lead to the need for industrial channel models to support massive multiple-input multipleoutput (MIMO) and millimeter wave communication. In addition, due to the complex environment, huge communication equipment, and numerous metal scatterers, industrial channels have special rich dense multipath components (DMCs). Considering these characteristics, a novel three dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for industrial automation wireless channel is proposed in this paper. Channel characteristics including the transfer function, time-varying space-time-frequency correlation function (STFCF), and root mean square (RMS) delay spread, model parameters including delay scaling factor and power decay factor are studied and analyzed. Besides, according to the indoor factory scenario classification of the 3rd Generation Partnership Project (3GPP) TR 38.901, two sub-scenarios considering the clutter density are simulated. Simulated cumulative distribution functions (CDFs) of RMS delay spread show a good consistency with the measurement data.

DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction

In spatial statistics, a common objective is to predict the values of a spatial process at unobserved locations by exploiting spatial dependence. In geostatistics, Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is not necessarily optimal, and the associated variance is often overly optimistic. We propose to use deep neural networks (DNNs) for spatial prediction. Although DNNs are widely used for general classification and prediction, they have not been studied thoroughly for data with spatial dependence. In this work, we propose a novel neural network structure for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. We show in theory that the proposed DeepKriging method has multiple advantages over Kriging and classical DNNs only with spatial coordinates as features. We also provide density prediction for uncertainty quantification without any distributional assumption and apply the method to PM$_{2.5}$ concentrations across the continental United States.