SNR Maximization and Localization for UAV-IRS-Assisted Near-Field Systems

TL;DR

This paper proposes a UAV-IRS-assisted near-field localization system that jointly optimizes UAV position and IRS beamforming to maximize SNR and improve localization accuracy, demonstrating superior performance through simulations.

Contribution

It introduces a novel UAV-IRS structure for near-field localization and develops an efficient joint optimization algorithm for enhanced performance.

Findings

The proposed algorithm outperforms existing schemes in SNR and localization accuracy.

Closed-form expressions for UAV-IRS phase shifts reduce computational complexity.

Simulation results validate the effectiveness of the joint optimization approach.

Abstract

This letter introduces a novel unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) structure into near-field localization systems to enhance the design flexibility of IRS, thereby obtaining additional performance gains. Specifically, a UAV-IRS is utilized to improve the harsh wireless environment and provide localization possibilities. To improve the localization accuracy, a joint optimization problem considering UAV position and UAV-IRS passive beamforming is formulated to maximize the receiving signal-to-noise ratio (SNR). An alternative optimization algorithm is proposed to solve the complex non-convex problem leveraging the projected gradient ascent (PGA) algorithm and the principle of minimizing the phase difference of the receiving signals. Closed-form expressions for UAV-IRS phase shift are derived to reduce the algorithm complexity. In the simulations, the proposed algorithm is compared with three different schemes and outperforms the others in both receiving SNR and localization accuracy.