Joint Trajectory and Beamforming Optimization for IRS-Assisted Multi-Antenna UAV Covert Communications With a Finite Blocklength
Wei Zhang, Xiaopeng Liang, Qian Deng, Feng Shu, Zhi Zhang, Liusong Nie, Shihao Yan
Abstract
A novel intelligent reflecting surface (IRS)-assisted multi-antenna unmanned aerial vehicle (UAV) covert communication (IRS-UAVCC) with a finite blocklength is studied, in which the IRS is exploited to enhance the covert transmission capability of moving multi-antenna UAV and weaken the detection capability of a warden. Our goal is to maximize the UAV covert transmission rate by jointly optimizing the multi-antenna UAV’s trajectory (UAVTR) and transmit beamforming (UAVTB), and the IRS’s phase shift matrix (PSM). To tackle this non-convex problem, we decompose it into three sub-problems. Firstly, the semidefinite relaxation (SDR) technique is employed to solve the two sub-problems of UAVTB and IRS’s PSM. Secondly, by applying the successive convex approximation (SCA) technique, the non-convex multi-antenna UAVTR optimization sub-problem is reformulated into a convex one. Finally, an efficient block coordinate descent (BCD) structure is proposed to obtain a suboptimal solution for the original optimization problem. To further reduce the complexity, a low complexity penalty dual decomposition with gradient projection (PDDGP) method is developed to obtain the optimal moving multi-antenna UAVTB and the IRS’s PSM. Numerical results demonstrate the effectiveness and superiority of the proposed algorithm in IRS-UAVCC with a finite blocklength.