Symbol-Level Precoding and Passive Beamforming Design for IRS-Aided Cognitive Radio Networks

Abstract

In this paper, we consider a joint beamforming design in a cognitive radio (CR) network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted at the base station to enhance the symbol error rate (SER) performance of the network. The joint beamforming design is formulated as a nonconvex optimization problem to achieve max-min fairness in the secondary network subject to the interference temperature constraints, the maximum power constraint, and the constant modulus constraints over the passive beamformer. To solve this problem with the coupling between variables, we propose an algorithm based on the alternating optimization (AO) technique, and then two subproblems can be obtained to optimize the transmit and passive beamformers alternately. Specifically, a penalized successive convex approximation (P-SCA) method is developed to optimize the passive beamformer. The simulation results demonstrate that the SLP technique can further enhance the system performance in terms of signal-to-interference-plus-noise ratio (SINR) compared with the conventional block-level precoding.