Machine Learning-Based Path Loss Modeling with Simplified Features

TL;DR

This paper introduces a simplified, environment-aware path loss modeling method using obstacle depth features, offering a practical alternative to complex models for wireless network planning.

Contribution

It presents a novel approach that leverages simplified environmental features, specifically obstacle depth, for accurate wireless propagation prediction.

Findings

Obstacle depth correlates strongly with path loss.

The method achieves comparable accuracy to detailed models.

Simplified features reduce computational complexity.

Abstract

Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning.