IRS-Enhanced Energy Detection for Spectrum Sensing in Cognitive Radio Networks

Energy detection is of crucial importance in cognitive radio networks. However, its performance is poor when the channel fading is severe, which causes interference to the primary users. In order to tackle this issue, an intelligent reflecting surface (IRS)-enhanced energy detection for spectrum sensing is proposed. Both the cases with and without the direct link between the primary user and the secondary user are considered. By using the Gamma distribution approximation and central limit theorem, the closed-form expressions for the average probability of detection are derived. In order to further improve the detection performance, IRS-enhanced energy detection for cooperative spectrum sensing and multiple IRSs-enhanced square-law selection diversity reception are also proposed. Expressions for the average probability of detection for these two schemes are provided by using the K-rank fushion criterion and square-law selection, respectively. Simulation results verify our theoretical analysis and demonstrate the superiority of our proposed IRS-enhanced energy detection compared with the benchmark schemes in terms of the spectrum sensing performance.

Automated summary

This paper proposes an intelligent reflecting surface (IRS)-enhanced energy detection method for spectrum sensing, which shows significant performance advantages in different scenarios. Through mathematical modeling and simulation experiments, the superiority of this method over conventional schemes is demonstrated.