Robust Directional Modulation Design for Secrecy Rate Maximization in Multi-User Networks

TL;DR

This paper proposes robust beamforming techniques based on directional modulation to maximize secrecy rates in multi-user networks, accounting for imperfect eavesdropper direction knowledge, and demonstrates improved performance over existing methods.

Contribution

It introduces two novel methods, VMD-SSRM and MAEE-SSRM, for secrecy rate maximization under direction angle estimation errors in multi-user systems.

Findings

VMD-SSRM outperforms ZF and SLNR methods in secrecy rate.

MAEE-SSRM performs better under worst-case estimation errors.

VMD-SSRM achieves higher secrecy rates than MAEE-SSRM in simulations.

Abstract

In this paper, based on directional modulation (DM), robust beamforming matrix design for sum secrecy rate maximization is investigated in multi-user systems. The base station (BS) is assumed to have the imperfect knowledge of the direction angle toward each eavesdropper, with the estimation error following the Von Mises distribution. To this end, a Von Mises distribution-Sum Secrecy Rate Maximization (VMD-SSRM) method is proposed to maximize the sum secrecy rate by employing semi-definite relaxation and first-order approximation based on Taylor expansion to solve the optimization problem. Then in order to optimize the sum secrecy rate in the case of the worst estimation error of direction angle toward each eavesdropper, we propose a maximum angle estimation error-SSRM (MAEE-SSRM) method. The optimization problem is constructed based on the upper and lower bounds of the estimated eavesdropping channel related coefficient and then solved by the change of the variable method. Simulation results show that our two proposed methods have better sum secrecy rate than zero-forcing (ZF) method and signal-to-leakage-and-noise ratio (SLNR) method. Furthermore, the sum secrecy rate performance of our VMD-SSRM method is better than that of our MAEE-SSRM method.