RCS-based Quasi-Deterministic Ray Tracing for Statistical Channel Modeling

TL;DR

This paper introduces a quasi-deterministic ray tracing method that uses statistical RCS modeling to efficiently analyze electromagnetic wave propagation in street canyons, matching deterministic models' accuracy with lower complexity.

Contribution

The paper proposes a novel QD-RT method utilizing statistical RCS for efficient electromagnetic propagation analysis in complex urban environments.

Findings

QD-RT matches D-RT path loss distributions

Lower computational complexity of QD-RT

Potential for mmWave street canyon analysis

Abstract

This paper presents a quasi-deterministic ray tracing (QD-RT) method for analyzing the propagation of electromagnetic waves in street canyons. The method uses a statistical bistatic distribution to model the Radar Cross Section (RCS) of various irregular objects such as cars and pedestrians, instead of relying on exact values as in a deterministic propagation model. The performance of the QD-RT method is evaluated by comparing its generated path loss distributions to those of the deterministic ray tracing (D-RT) model using the Two-sample Cramer-von Mises test. The results indicate that the QD-RT method generates the same path loss distributions as the D-RT model while offering lower complexity. This study suggests that the QD-RT method has the potential to be used for analyzing complicated scenarios such as street canyon scenarios in mmWave wireless communication systems.