Joint UAV Placement and IRS Phase Shift Optimization in Downlink Networks

TL;DR

This paper proposes a joint optimization framework for UAV placement, IRS phase shifts, and beamforming in downlink networks to enhance spectral efficiency and coverage in urban environments with obstructed links.

Contribution

It introduces an iterative algorithm for jointly optimizing UAV position, IRS phase shifts, and beamforming, addressing the non-convexity of the problem in IRS-UAV systems.

Findings

Significant sum rate improvements over terrestrial systems.

Effective joint optimization of UAV placement and IRS phase shifts.

Validation through numerical simulations.

Abstract

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS-UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.