Navigation of a UAV Equipped with a Reconfigurable Intelligent Surface for LoS Wireless Communication with a Ground Vehicle

TL;DR

This paper proposes a UAV equipped with reconfigurable intelligent surfaces to maintain a secure, uninterrupted LoS communication link with a ground vehicle, optimizing trajectory and beamforming for critical medical data transmission.

Contribution

It introduces a novel RIS-equipped UAV system and a two-stage optimization method for trajectory and beamforming to enhance secure LoS communication with ground targets.

Findings

Effective trajectory optimization improves LoS link stability.

Simulation confirms enhanced communication performance.

Method reduces energy consumption while maintaining link quality.

Abstract

Unmanned aerial vehicles (UAVs) have been successfully adopted to enhance the flexibility and robustness of wireless communication networks. And recently, the reconfigurable intelligent surface (RIS) technology has been paid increasing attention to improve the throughput of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication. In this work, we propose an RIS-outfitted UAV (RISoUAV) to secure an uninterrupted line-of-sight (LoS) link with a ground moving target (MT). The MT can be an emergency ambulance and need a secure wireless communication link for continuous monitoring and diagnosing the health condition of a patient, which is vital for delivering critical patient care. In this light, real-time communication is required for sending various clinical multimedia data including videos, medical images, and vital signs. This significant target is achievable thanks to the 5G wireless communication assisted with RISoUAV. A two-stage optimization method is proposed to optimize the RISoUAV trajectory limited to UAV motion and LoS constraints. At the first stage, the optimal tube path of the RISoUAV is determined by taking into account the energy consumption, instant LoS link, and UAV speed/acceleration constraints. At the second stage, an accurate RISoUAV trajectory is obtained by considering the communication channel performance and passive beamforming. Simulation results show the accuracy and effectiveness of the method.