Active RIS Enhanced Spectrum Sensing for Opportunistic Cognitive Radio Networks

In opportunistic cognitive radio networks, the secondary user (SU) requires long sensing time to achieve a reliable spectrum sensing performance when the primary signal is very weak, leading to little remaining time for the secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system to enhance the received primary signal at the SU, therefore the required sensing time can be reduced. In contrast to the passive RIS, the active RIS can amplify the incident signal and hence is more efficient in terms of the required reflecting elements as well as power consumption. Particularly, we study the reflecting coefficient matrix (RCM) optimization problem to improve the performance of the active RIS assisted spectrum sensing system. With the knowledge of the spiked model from random matrix theory, the RCM optimization problem can be transformed to an equivalent problem maximizing the largest eigenvalue of the population covariance matrix of the sensing signal samples. Then, we adopt the weighted minimum mean square error (WMMSE) algorithm to obtain the optimal RCM. Besides, we also investigate the minimum power budget for the active RIS to realize a near-1 detection probability under a simplified case, where the direct link does not exist and line-of-sight RIS-related channels are considered. Simulation results show that the active RIS can outperform the passive RIS for the same power budget in the RIS-assisted spectrum sensing system.