Analysis of Factors Affecting Spectrum Sensing in an IRS based Wireless Network

Intelligent reflecting surface, or IRS, has shown great promise for wireless networks. IRS provides flexible wireless channel control and setup by dynamically adjusting the reflection amplitudes/phase shifts of numerous devices. This greatly increases the wireless signal transmission rate and dependability. In cognitive radio networks, spectrum sensing and communication security are critical components. Intelligent reflecting surfaces (IRS) to improve the sensing performance and the accuracy of spectrum sensing at the same time. This work and through MATLAB simulation, carry out analysis of the effect of user traffic (Tuser), noise factor (Nfactor), and probability of false alarm (Pfalse) on the ability of an IRS based wireless system to spectrum sense through computation of detection probability (Pdetection). The work provided results, analysis, and mathematical model for both stable environmental conditions, and unstable ones. In addition, two Signal-to-Noise Ratio (SNR) levels are taken into account, 20% and 80% noise. This enables assessing of spectrum sensing under different conditions. The results shows that Pdetection decreases as Nfactor increases per percentage of Tuser with a logarithmic shape function. The work also uncovers that there is a tenfold increase in the noise factor as the user traffic level goes above 50%. As the noise factor increases for a fixed probability of false alarm, and with user traffic increase, detection probability decreases, and as Pfalse increases, so does the Pdetection. The obtained results indicate that there should be a balance in Tuser with Nfactor in order to optimize Pdetection and reduce the effect of Pfalse in spectrum sensing