Active Reconfigurable Intelligent Surface-Aided Cognitive Radio System

This paper considers an active reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) cognitive radio (CR) system where the active RIS is designed to assist the transmission of the secondary user (SU) by tuning its controllable reflecting elements (REs) with enhanced reflections. As each RE in active RISs can adjust the phases and amplify the incident signals, active RISs thus provide a more favorable channel condition for the considered CR system than conventional passive RISs do. We aim to maximize the achievable rate of the SU subject to the interference temperature (IT) constraints on the primary users (PUs) as well as the power budget constraint on the active RIS. Towards this end, we propose an alternating optimization algorithm to solve this rate maximization problem. More specifically, the transmit beamforming vector is obtained by solving a second-order core programming (SOCP) problem, and the reflect beamforming vector is obtained with the aid of the fractional programming (FP) technique. Numerical results are provided to compare the active RIS-aided CR system with the passive RIS-aided one, showing that under the same power budget, the active RIS notably outperforms the passive RIS when they are used to assist the transmission of the SU in the CR system.