The Performance Analysis of Double Intelligent Reflecting Surface-Assisted Cognitive Radio Network

Intelligent reflecting surfaces (IRS) can efficiently improve the performance of wireless communication networks by intelligently reconfiguring the wireless propagation environment. Recently, IRS has been integrated with cognitive radio (CR) network to improve the resource utilization of communication systems. In this paper, we investigate the optimization of downlink rate of the secondary user (SU) in a double-IRS assisted CR network. The achievable rate is maximized by jointly optimizing the active beamforming vector at the secondary transmitter (SU-TX) and the reflection coefficients at the two distributed IRSs. To solve the proposed non-convex joint optimization problem, we employ alternating optimization (AO) and semidefinite relaxation (SDR) techniques to iterate the optimization variables. Numerical results validate that the proposed double-IRS assisted system can significantly improve the performance of the CR network compared to the existing single-IRS assisted CR system.