Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Intelligent Reflecting Surface (IRS) is a new promising technology that can enhance the performance of cognitive radio (CR) networks by improving the spectrum sensing and communication efficiency. In this paper, we propose an IRS-aided spectrum sensing scheme for CR networks. The proposed scheme utilizes the passive reflecting property of IRS to enhance the signal-to-noise ratio (SNR) of the received signal at the CR receiver. The IRS reflects the received signal to enhance the received power and reduce the interference from other users in the network. The proposed scheme also uses machine learning techniques to adaptively adjust the reflecting coefficients of the IRS to maximize the SNR of the received signal.

Simulation results show that the proposed scheme outperforms the conventional spectrum sensing scheme in terms of detection probability and false alarm rate. The simulation results also show that the proposed scheme can achieve a higher SNR with fewer samples than the conventional scheme. Moreover, the proposed scheme can improve the communication efficiency of the CR network by reducing the interference from other users in the network.

In conclusion, the proposed IRS-aided spectrum sensing scheme can significantly enhance the performance of CR networks. The scheme can improve the spectrum sensing accuracy and communication efficiency by utilizing the passive reflecting property of IRS and the machine learning techniques to adaptively adjust the reflecting coefficients of the IRS. The proposed scheme has great potential in future CR networks to address the increasing demand for spectrum resources.