Intelligent Reflecting Surface Empowered UAV SWIPT Networks

Aiming at the limited battery capacity of a large number of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the energy transmission efficiency and coverage of the UAV SWIPT networks. In this paper, we formulate an achievable sum-rate maximization problem by jointly optimizing UAV trajectory, UAV transmission power allocation, power splitting (PS) ratio and IRS reflection coefficient under a non-linear energy harvesting model. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. We first transform the problem, and then apply the alternating optimization (AO) algorithm framework to divide the transformed problem into four blocks to solve it. Specifically, by applying successive convex approximation (SCA) and difference-convex (DC) programming, UAV trajectory, UAV transmission power allocation, PS ratio and IRS reflection coefficient are alternately optimized when the other three are given until convergence is achieved. Numerical simulation results verify the effectiveness of our proposed algorithm compared to other algorithms.