High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements

TL;DR

This paper introduces a novel 3D radio map estimation method using Gaussian Process Regression, achieving high accuracy with significantly fewer measurements in complex urban environments, and provides a large-scale dataset for future research.

Contribution

The paper presents a new 3D radio map estimation scheme based on Gaussian Process Regression and introduces two data-efficient training point selection methods, along with a large-scale urban 3D radio map dataset.

Findings

Achieves over 2.5 dB lower RMSE than state-of-the-art methods.

Full map recovery with only 2% of UAV measurements.

Provides a large-scale 3D radio map dataset for urban environments.

Abstract

Recent widespread applications for unmanned aerial vehicles (UAVs) -- from infrastructure inspection to urban logistics -- have prompted an urgent need for high-accuracy three-dimensional (3D) radio maps. However, existing methods designed for two-dimensional radio maps face challenges of high measurement costs and limited data availability when extended to 3D scenarios. To tackle these challenges, we first build a real-world large-scale 3D radio map dataset, covering over 4.2 million m^3 and over 4 thousand data points in complex urban environments. We propose a Gaussian Process Regression-based scheme for 3D radio map estimation, allowing us to realize more accurate map recovery with a lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance data efficiency, we propose two methods for training point selection, including an offline clustering-based method and an online maximum a posterior (MAP)-based method. Extensive experiments demonstrate that the proposed scheme not only achieves full-map recovery with only 2% of UAV measurements, but also sheds light on future studies on 3D radio maps.