MD-GAN-Based UAV Trajectory and Power Optimization for Cognitive Covert Communications

Abstract

This article investigates the covert performance of an unmanned aerial vehicle (UAV) jammer-assisted cognitive radio (CR) network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of an eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN)-assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then, a GAN-based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of the UAV's trajectory and transmits power to maximize the covert rate and the probability of detection errors. Our simulation results show that the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of the UAV's trajectory and transmit power for the covert communication.