Abstract:

Aiming at the covert communication scenario of an unmanned aerial vehicle(UAV) jammer assisted cognitive radio network,a transferred generative adversarial network based resource optimization algorithm is proposed for the UAV’s joint trajectory and transmit power optimization problem.First,based on the actual covert communication scenario,the UAV jammer assisted cognitive covert communication model is constructed.Then,a transferred generative adversarial network based resource allocation algorithm is designed,which introduces a transfer learning and generative adversarial network.The algorithm consists of a source domain generator,a target domain generator,and a discriminator,which extract the main resource allocation features of legitimate users not transmitting covert message by transfer learning,then transform the whole covert communication process into an interactive game between the legitimate users and the eavesdropping,alternatively train the target domain generator and discriminator in a competitive manner,and achieve the Nash equilibrium to obtain resource optimization solution for the covert communications.Numerical results show that the proposed algorithm can attain near-optimal resource optimization solution for the covert communication and achieve rapid convergence under the assumptions of knowing the channel distribution information and not knowing the detection threshold of the eavesdropper.

Key words: covert communication, unmanned aerial vehicle, resource optimization, transfer learning, generative adversarial network