Secrecy Rate Maximization for STAR-RIS-Assisted MIMO Cognitive Radio Networks

In this letter, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secondary transmitter (ST) sends confidential information to a secondary user while sharing the same frequency band with a primary user in the presence of an eavesdropper. We aim to maximize the secrecy rate of the secondary system by jointly optimizing the transmit precoding matrices at the ST, along with the transmission and reflection coefficients of the STAR-RIS. An efficient alternating optimization algorithm based on the penalty method and the generalized S-procedure is proposed to solve the formulated non-convex problem. Simulation results show that the proposed scheme significantly outperforms the conventional RIS in improving the secrecy rate.