Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based approach for predicting path loss in multiple cities by automatically extracting features from 2-D obstruction maps, improving accuracy without relying on derived metrics.

Contribution

The paper presents a novel CNN method that directly uses 2-D obstruction height maps for path loss prediction across various environments, eliminating the need for handcrafted metrics.

Findings

Low prediction error across different city environments

Effective automatic feature extraction from obstruction maps

Improved accuracy over traditional methods

Abstract

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.