mmWave Communications for Indoor Dense Spaces: Ray-Tracing Based Channel Characterization and Performance Comparison

Abstract

In this paper, the indoor dense space (IDS) channel at 28 GHz is characterized through extensive Ray-Tracing (RT) simulations. We consider IDS as a specific type of indoor environment with confined geometry and packed with humans, such as aircraft cabins and train wagons. Based on RT simulations, we characterize path loss, shadow fading, root-mean-square delay spread, Rician K-factor, azimuth/elevation angular spread of arrival/departure considering different RT simulation scenarios of the fuselage geometry, material, and human presence. While the large-scale fading parameters are similar to the stateof-the-art channel models, the small-scale fading parameters demonstrate richer multipath scattering in IDS, resulting in poorer bit error rate performance in comparison to the 3GPP indoor channel model.

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

“…Especially at high frequencies (mmWave communications), reflections and diffractions are very sensitive to variations in the indoor propagation environment [12]. To estimate the Path Loss (PL) in these indoor environments a PL calculation based on ray-tracing (RT) models is more accurate than the use of traditional statistical models [13], [14].…”