智能反射面辅助的无人机无线携能通信网络吞吐量最大化算法研究

为了解决城市场景中无人机(UAV)与地面终端设备(GUs)间易受到障碍物阻挡的问题，该文提出一种基于智能反射面(IRS)辅助的UAV供能通信网络吞吐量最大化算法。首先，在满足能量因果、IRS相移、UAV移动性等约束条件下，建立了一个联合IRS相移设计、GU无线资源分配、UAV飞行轨迹设计的多变量耦合优化模型。其次，通过快坐标下降法(BCD)将原非凸问题转换为3个易于处理的子问题，并通过三角不等式、引入松弛变量、连续凸近似(SCA)等方法，对子问题进行转化求解。仿真结果表明，该文所提算法具有较好的收敛性，同时可有效提高系统总吞吐量。

Throughput Maximization Algorithm for Intelligent Reflecting Surface-aided Unmanned Aerial Vehicle Communication Networks with Wireless Energy Transfer

In order to mitigate the adverse effect of blockages between the Unmanned Aerial Vehicle (UAV) and Ground Users (GUs), a throughput maximization algorithm for an Intelligent Reflecting Surface (IRS)-aided UAV communication network is proposed. First, considering the constraints of the energy causality, the IRS phase-shift, the UAV mobility, etc, a multi-variable coupling optimization problem is proposed with jointly optimizing the phase-shift of the IRS, the resource allocation of GUs, and the UAV trajectory. Second, the original non-convex problem is decomposed into three simpler sub-problems via the Block Coordinate Descent (BCD), which are tackled by the triangle inequality, introducing the slack variables and Successive Convex Approximation (SCA). Numerical results show that the proposed algorithm has a desirable convergence, as well as improves effectively the system sum-throughput.