Reconfigurable Intelligent Surface Enabled Cognitive Radio Networks

In this paper, we investigate the impact of integrating reconfigurable intelligent surface (RIS) in cognitive radio (CR) networks for the enhancement of spectrum sensing and data transmission for unlicensed users (ULU) in a limited energy environment. The idea is to use RIS to help ULU in spectrum sensing as well as energy harvesting operations so that the CR networks perform more efficiently and have better spectral efficiency. To get the full potential of RIS, we employed a nature-inspired soft computing technique named Grey-Wolf optimization (GWO) to tune the reflecting phase of the RIS as needed. With the support of RIS, we investigate the usage of non-orthogonal multiple access (NOMA) technique for data transmission across multiple users. We illustrate the effectiveness of this integrated approach through simulation and provide the receiver operating characteristics (ROC) curve for spectrum sensing performance and outage probability for spectral efficiency.