Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks

TL;DR

This paper introduces a novel IRS-empowered UAV SWIPT network framework that enhances wireless energy transfer and coverage by jointly optimizing trajectory, power, and IRS reflection, addressing IoT device power limitations.

Contribution

It proposes a joint optimization framework for UAV trajectory, power, and IRS reflection in SWIPT networks, solving a complex non-convex problem with an alternating optimization approach.

Findings

Proposed algorithm outperforms existing methods in sum-rate maximization.

Joint optimization significantly improves energy transfer efficiency.

Numerical results confirm the effectiveness of the proposed framework.

Abstract

Aiming at the limited battery capacity 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 wireless energy transmission efficiency and coverage area of the UAV SWIPT networks. In this paper, we formulate an achievable sum-rate maximization problem by jointly optimizing UAV trajectory, successive interference cancellation (SIC) decoding order, UAV transmit power allocation, power splitting (PS) ratio and IRS reflection coefficient while taking account of user non-orthogonal multiple access (NOMA) and 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 sub-problems to solve it. Specifically, by applying successive convex approximation (SCA), penalty function method and difference-convex (DC) programming, UAV trajectory, SIC decoding order, UAV transmit power allocation, PS ratio and IRS reflection coefficient are alternately optimized until the convergence is achieved. Numerical simulation results verify the effectiveness of our proposed algorithm compared to other algorithms.