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Title: Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach
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.  more » « less
Award ID(s):
2120442
PAR ID:
10331757
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2022 IEEE Wireless Communications and Networking Conference (WCNC)
Page Range / eLocation ID:
2470 to 2475
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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