Line-of-Sight Probability in Macrocells: Framework, Statistical Models, and Parametrization from Massive Real World Datasets in the USA

TL;DR

This paper develops a high-accuracy LOS probability model for US macrocells using extensive real-world datasets, improving coverage planning and interference prediction over existing models.

Contribution

It introduces a new framework for modeling LOS probability from geospatial data and creates a fully parameterized, per-cell LOS model that outperforms existing standards.

Findings

The new model better predicts macrocell LOS probability in the US.

Per-cell modeling with random parameters improves interference prediction.

The framework leverages large-scale datasets for high-accuracy LOS modeling.

Abstract

Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread, etc.. Existing models, although useful, are based on very limited datasets. In this paper, we establish a framework to produce high accuracy LOS models from geospatial data in different environments, and apply it to create a LOS model for macrocells, using datasets of the United States (US) on a nationalscale, using more than 13, 000 locations of real-world macrocells. Based on this we create a new, fully parameterized model that better describes macrocell deployments in the US than the 3GPP model. We furthermore demonstrate that for improved accuracy the LOS probability should be modeled on a per cell basis, and the model parameters treated as random variables; we provide a full description and parameterization of this novel approach and by simulations show that it better predicts the inter-cell interference at the cell-edge than an average-based model.