Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network

TL;DR

This paper proposes a deep learning method to optimize UAV base station altitude using satellite images, predicting path loss distributions to maximize coverage without extensive measurements.

Contribution

It introduces a novel deep neural network approach that predicts multiple path loss distributions from satellite images for altitude optimization.

Findings

Neural network accurately predicts path loss at various altitudes.

The method maximizes coverage by selecting optimal UAV altitude.

Single inference predicts multiple path loss scenarios.

Abstract

It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The predicted path distributions are used to calculate the coverage in the region; and the optimum altitude, maximizing the coverage, is determined. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.