A Prediction Study of Path Loss Models from 2-73.5 GHz in an Urban-Macro Environment

TL;DR

This study compares physics-based and curve-matching path loss models across 2-73.5 GHz in urban macro environments, demonstrating that physics-based models offer better prediction accuracy and stability for 5G frequency ranges.

Contribution

It provides a comprehensive evaluation of path loss models using real measurements and ray-tracing, highlighting the advantages of physics-anchored models for 5G system assessment.

Findings

Physics-anchored models predict path loss more accurately in diverse scenarios.

Models with physical anchors are more stable across different measurement sets.

Ray-tracing and real-world data validate the effectiveness of physics-based models.

Abstract

It is becoming clear that 5G wireless systems will encompass frequencies from around 500 MHz all the way to around 100 GHz. To adequately assess the performance of 5G systems in these different bands, path loss (PL) models will need to be developed across this wide frequency range. The PL models can roughly be broken into two categories, ones that have some anchor in physics, and ones that curve-match only over the data set without any physical anchor. In this paper we use both real-world measurements from 2 to 28 GHz and ray-tracing studies from 2 to 73.5 GHz, both in an urban-macro environment, to assess the prediction performance of the two PL modeling techniques. In other words, we look at how the two different PL modeling techniques perform when the PL model is applied to a prediction set which is different in distance, frequency, or environment from a measurement set where the parameters of the respective models are determined. We show that a PL model with a physical anchor point can be a better predictor of PL performance in the prediction sets while also providing a parameterization which is more stable over a substantial number of different measurement sets.