Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledge

TL;DR

This paper introduces a deep learning model that uses geographical images and expert knowledge to predict radio signal strength more accurately than traditional empirical models, reducing errors significantly.

Contribution

The paper presents a novel model-aided deep learning approach that implicitly extracts radio propagation features from geographical images for improved path loss prediction.

Findings

Reduces average prediction error by up to 53% compared to ray-tracing.

250-300 meters image span provides sufficient detail.

Achieves approximately 6 dB RMS error across diverse data sources.

Abstract

Methods for accurate prediction of radio signal quality parameters are crucial for optimization of mobile networks, and a necessity for future autonomous driving solutions. The power-distance relation of current empirical models struggles with describing the specific local geo-statistics that influence signal quality parameters. The use of empirical models commonly results in an over- or under-estimation of the signal quality parameters and require additional calibration studies. In this paper, we present a novel model-aided deep learning approach for path loss prediction, which implicitly extracts radio propagation characteristics from top-view geographical images of the receiver location. In a comprehensive evaluation campaign, we apply the proposed method on an extensive real-world data set consisting of five different scenarios and more than 125.000 individual measurements. It is found that 1) the novel approach reduces the average prediction error by up to 53% in comparison to ray-tracing techniques, 2) A distance of 250-300 meters spanned by the images offer the necessary level of detail, 3) Predictions with a root-mean-squared error of approximately 6 dB is achieved across inherently different data sources.