GPI-based Secrecy Rate Maximization Beamforming Scheme for Wireless Transmission with AN-aided Directional Modulation

TL;DR

This paper introduces a GPI-based beamforming scheme that optimizes secrecy rate in directional modulation networks by iteratively adjusting precoding and artificial noise projection, significantly improving security performance at higher SNRs.

Contribution

It proposes a novel GPI-based iterative method for maximizing secrecy rate by jointly optimizing precoding and artificial noise projection in directional modulation systems.

Findings

Achieves faster convergence with only four iterations.

Outperforms conventional methods in medium and large SNR regions.

Provides increasing secrecy rate gains as SNR increases.

Abstract

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases.