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

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

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。

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.