# On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks

arXiv: https://arxiv.org/abs/1804.08415 · HTML: https://mlsanity.com/paper/1804.08415/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems](https://mlsanity.com/paper/2302.04445/) (2023)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models](https://mlsanity.com/paper/2401.13827/) (2024)
- [UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations](https://mlsanity.com/paper/2202.10338/) (2022)
- [Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network](https://mlsanity.com/paper/2112.14551/) (2021)

## Abstract (arXiv)

Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network.