# Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks

arXiv: https://arxiv.org/abs/1603.00300 · HTML: https://mlsanity.com/paper/1603.00300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2016.

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

- [Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning](https://mlsanity.com/paper/2106.00845/) (2021)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [Energy-Efficient Cellular-Connected UAV Swarm Control Optimization](https://mlsanity.com/paper/2303.10398/) (2023)
- [Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint](https://mlsanity.com/paper/2109.05767/) (2021)
- [Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV](https://mlsanity.com/paper/2605.04436/) (2026)
- [Deep Reinforcement Learning-Empowered Wireless Sensor Networking for 6G Closed-Loop Controls](https://mlsanity.com/paper/2607.08272/) (2026)

## Abstract (arXiv)

Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the dronecell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.