Robust Transmission Design for IRS-Aided Secure Cognitive Radio Systems Against Internal Eavesdropping

A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.