Two Low-complexity Efficient Beamformers for IRS-and-UAV-aided Directional Modulation Networks

TL;DR

This paper introduces two low-complexity, efficient beamforming methods for IRS-and-UAV-assisted directional modulation networks to enhance secrecy rate performance, balancing complexity and effectiveness.

Contribution

It proposes two novel methods, Max-SR-SLNR and MRT-NSP-PA, for improving secrecy rate with reduced computational complexity in IRS-UAV networks.

Findings

Max-SR-SLNR outperforms MRT-NSP-PA in small and medium IRS scales.

MRT-NSP-PA approaches Max-SR-SR in large-scale IRSs.

Both methods improve secrecy rate performance.

Abstract

As the excellent tools for aiding communication,intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) can extend the coverage area, remove blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance at directional modulation (DM) networks using IRS and UAV in combination. To fully explore the benefits of IRS and UAV, two efficient methods are proposed to enhance 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, which is called Max-SR-SLNR. Here, Eve is maximally interfered by transmitting artificial noise (AN) along the direct path and null-space projection (NSP) on the remaining two channels. To reduce the computational complexity, the CM, AN beamforming and IRS phase shift design are independently designed in the following methods. The CM beamforming vector is constructed based on maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, and phase shift matrix of IRS is directly given by phase alignment (PA) method. This method is called MRT-NSP-PA. 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 IRS tends to lager-scale.