Outage Constrained Secrecy Rate Maximization of Intelligent Reflecting Surface Aided Transmission

Abstract

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of the eavesdropper and the cascaded channel brought by the IRS, the eavesdropper’s channel state information is imperfectly obtained at the base station. Under the channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper formulates a secrecy rate maximization problem while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling variables, the secrecy rate maximization problem can be solved via alternatively executing difference-of-convex programming and semidefinite relaxation method. The simulation results validate the strength of this newly established transmission scheme when compared to baseline schemes of random phase-shift, fixed phase-shift, and IRS ignoring CSI uncertainty.