Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks

TL;DR

This paper introduces a deep convolutional neural network approach for radio propagation modeling that outperforms traditional models and allows for the integration of diverse site information for improved accuracy.

Contribution

The paper presents a novel deep learning framework for radio propagation modeling, enhancing accuracy and flexibility over conventional methods.

Findings

Significantly improved performance over traditional models

Framework enables utilization of rich site information

Supports future research with unconventional data sources

Abstract

Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps. In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.