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

Automated summary

This paper investigates the use of intelligent reflecting surface (IRS) to enhance the network performance of cognitive radio (CR) systems. It focuses on optimizing the transmit precoding (TPC) and phase shifts at the IRS to minimize total transmit power while ensuring quality of service for secondary users (SUs), limited interference on primary users (PUs), and unit-modulus of the reflective beamforming. The paper proposes efficient algorithms to transform the non-convex optimization problems into convex subproblems and verifies their effectiveness through simulation results. The impact of CSI uncertainties on the minimum transmit power and feasibility rate of the optimization problems is also examined.