Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach
This addresses physical layer security for UAV-assisted wireless networks, but it is incremental as it applies existing DQN methods to a specific scenario.
The paper tackled the problem of eavesdropping on wireless links between ground users and aerial base stations by optimizing ABS position and transmission power using reinforcement learning, resulting in the highest secrecy capacity with fast convergence compared to baseline methods.
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.