# Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach

arXiv: https://arxiv.org/abs/2112.11090 · HTML: https://mlsanity.com/paper/2112.11090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1707.07534/) (2017)
- [Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs](https://mlsanity.com/paper/1908.07774/) (2019)
- [UAV-Assisted Attack Prevention, Detection, and Recovery of 5G Networks](https://mlsanity.com/paper/2007.00244/) (2020)
- [Securing Mobile IoT with Unmanned Aerial Systems](https://mlsanity.com/paper/2005.07363/) (2020)
- [Securing Mobile Multiuser Transmissions with UAVs in the Presence of Multiple Eavesdroppers](https://mlsanity.com/paper/2009.03557/) (2020)

## Built on by (most cited first, 1 of 1)

- [UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI](https://mlsanity.com/paper/2306.06686/) (2023)

## Abstract (arXiv)

The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAV will play a critical role in enhancing the physical layer security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and baseline approaches.