Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems With Imperfect Cascaded CSI

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI for primary user (PU)-related channels in IRS-aided CR We jointly optimize transmit precoding (TPC) at secondary (SU) transmitter (ST) phase shifts IRS minimize ST’s total power subject quality service SUs, limited interference imposed PU unit-modulus reflective beamforming. The successive convex approximation (SCA) method, Schur’s complement, General sign-definiteness principle, inverse Chi-square distribution penalty convex-concave procedure are invoked dealing with these intricate constraints. non-convex optimization problems transformed into several subproblems efficient algorithms proposed. Simulation results verify efficiency proposed reveal impacts uncertainties minimum feasibility rate problems. also show that number antennas ST should be carefully chosen balance realization power.