Journal article
A. S. Abdalla, Ali Behfarnia, V. Marojevic
2022 IEEE Wireless Communications and Networking Conference (WCNC), 2021
2022 IEEE Wireless Communications and Networking Conference (WCNC), 2021
Semantic Scholar
ArXiv
DBLP
DOI
Cite
Cite
APA
Click to copy
Abdalla, A. S., Behfarnia, A., & Marojevic, V. (2021). Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach. 2022 IEEE Wireless Communications and Networking Conference (WCNC).
Chicago/Turabian
Click to copy
Abdalla, A. S., Ali Behfarnia, and V. Marojevic. “Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach.” 2022 IEEE Wireless Communications and Networking Conference (WCNC) (2021).
MLA
Click to copy
Abdalla, A. S., et al. “Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach.” 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2021.
BibTeX Click to copy
@article{a2021a,
title = {Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach},
year = {2021},
journal = {2022 IEEE Wireless Communications and Networking Conference (WCNC)},
author = {Abdalla, A. S. and Behfarnia, Ali and Marojevic, V.}
}
Abstract
The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the 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 two baseline approaches.
