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

arXiv: https://arxiv.org/abs/2112.14551 · HTML: https://mlsanity.com/paper/2112.14551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1703.00737/) (2017)
- [On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks](https://mlsanity.com/paper/1804.08415/) (2018)
- [Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis](https://mlsanity.com/paper/1509.01655/) (2015)

## Abstract (arXiv)

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.