Document Type

Journal Article

Publication Title

Drones

Volume

7

Issue

8

Publisher

MDPI

School

School of Science / Security Research Institute

RAS ID

62066

Funders

This work was supported in part by the National Natural Science Foundation of China (Nos. U22A2002, 62071234, and 61972093), the Hainan Province Science and Technology Special Fund (ZDKJ2021022), the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008, and the Fujian University Industry University Research Joint Innovation Project (No. 2022H6006).

Comments

Lin, Y., Shu, F., Zheng, Y., Liu, J., Dong, R., Chen, X., ... & Wang, J. (2023). Two low-complexity efficient beamformers for an IRS- and UAV-aided directional modulation network. Drones, 7(8), 489. https://doi.org/10.3390/drones7080489

Abstract

As excellent tools for aiding communication, an intelligent reflecting surface (IRS) and an unmanned aerial vehicle (UAV) can extend the coverage area, remove the blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance of directional modulation (DM) networks using an IRS and UAV in combination. To fully explore the benefits of the IRS and UAV, two efficient methods are proposed to enhance the SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix (PSM), which is called Max-SR-SLNR. To reduce the computational complexity, the CM, artificial noise (AN) beamforming, and IRS phase shift design are independently designed in the following method. The CM beamforming vector is constructed based on the maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, the AN beamforming vector is designed by null-space projection (NSP) on the remaining two channels, and the PSM of the IRS is directly given by the phase alignment (PA) method. This method is called the MRT-NSP-PA. The simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as the IRS tends to a larger scale.

DOI

10.3390/drones7080489

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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