无人机干扰辅助认知隐蔽通信资源优化算法

Resource optimization algorithm for unmanned aerial vehicle jammer assisted cognitive covert communications

面向无人机干扰辅助下的认知无线电网络隐蔽通信场景,针对无人机干扰源飞行轨迹和发送功率联合优化问题,提出了一种基于迁移式生成对抗网络的资源优化算法。首先,从实际隐蔽通信场景出发,构建了无人机干扰辅助认知隐蔽通信模型;其次,引入迁移学习和生成对抗网络思想,设计了基于迁移式生成对抗网络的资源优化算法,主要由源域生成器、目标域生成器和鉴别器组成。通过迁移学习来提取进行隐蔽通信时合法用户的资源分配主要特征,然后将隐蔽通信问题转化为合法用户与窃听者之间的动态博弈问题,以竞争的方式交替训练目标域生成器和鉴别器,达到纳什均衡,得到隐蔽通信资源优化方案。仿真结果表明,该算法能够在已知窃听者信道分布信息和未知窃听者检测阈值的情况下,生成近似最优的隐蔽通信资源优化方案,并且具有快速收敛的能力。

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.