## 4 Simulation Results and Discussion  
The results of Monte-Carlo simulations are provided in this section to verify the closed-form expressions derived in Section 3. All the parameters utilized in this analysis are listed in Table 2. The power fraction allocation, desired rates, average channel gains are selected based on [30; 31]. Figure 3 compares the probability of outages for FNOMA, ONOMA, and RIS-SR-ONOMA for 32 passive reflector elements. The probability of outage expressions for \(SU\) and \(WU\) in FNOMA system are given by [28]  
\[P_{WU}^{\text{FNOMA}}=1-exp\left(-\frac{D_{WU}}{(\alpha_{1}-\alpha_{2}D_{WU} )\varrho_{s}\delta_{1}^{2}}\right) \tag{44}\]  
\[P_{SU}^{\text{FNOMA}}=1-exp\left(\frac{-y_{SU}}{\varrho_{s}\delta_{2}^{2}}\right) \tag{45}\]  
The probability of outage expressions for \(SU\) and \(WU\) in ONOMA system are given by [8; 28]  
\[P_{WU}^{\text{ONOMA}}=1-exp\left(-\frac{D_{WU}}{(\alpha_{1}-\alpha_{2}D_{WU} )\varrho_{s}\delta_{H}^{2}}\right) \tag{46}\]  
\[P_{SU}^{\text{ONOMA}}=1-exp\left(\frac{-y_{SU}}{\varrho_{s}\delta_{1}^{2}} \right)-exp\left(\frac{-y_{SU}}{\varrho_{s}\delta_{2}^{2}}\right)+exp\left( \frac{-y_{SU}}{\varrho_{s}\delta_{H}^{2}}\right) \tag{47}\]At 5 dB SNR, the probabilities of outage are 0.3266, 0.3777, and 0.02921 for ENOMA-WU, ONOMA-WU and RIS-SR-ONOMA-WU respectively. Similarly, the probabilities of outage are 0.4697, 0.4488, and 0.0006951 for ENOMA-SU, ONOMA-SU and RIS-SR-ONOMA-SU respectively. It is observed that probability of outage performance of ENOMA is better for _WU_. ONOMA reduces the probability of outage of _SU_ compared to ENOMA. A great improvement in outage probabilities can be seen when RIS is incorporated with ONOMA for _SU_ and _WU_. Figure 4 depicts a probability of outage analysis for blind RIS-SR-ONOMA _SU_ for various \(N\) values. It is observed that probability of outage performance of _SU_ is better than _WU_ for various \(N\). A deviation in simulated and theoretical results is observed at lower values of \(N\), such as 4, 8 and 16. As per (3) and (7), the principle of CLT is violated for lower values of \(N\). After \(N=32\), the simulated and theoretical results became closely related, so the lower limit of \(N=32\) is fixed for the rest of the work. For \(N=32\), 64, 128, 256, and 512, \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Quantity of users & 2 \\ Quantity of reflecting elements & 4,8,16,32,64,128,256,512 \\ Average channel gain of _WU_ & 1 \\ Average channel gain of _SU_ & 5 \\ Target rate of _WU_ & 1 b/s/Hz \\ Target rate of _SU_ & 1 b/s/Hz \\ Target throughput & 3 b/s/Hz \\ Power factor allocated to _WU_ & 0.1 \\ Power factor allocated to _SU_ & 0.9 \\ Block length & \(10^{6}\) bits \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in this analysis [8; 30; 31; 28; 32; 11]. Figure 3: Comparison of probabilities of outage of _WU_ and _SU_ for ENOMA, ONOMA and blind RIS-SR-ONOMA. the probabilities of outage are 0.006951, 0.001841, 0.0004741, 0.0001203 and \(3.03\times 10^{-5}\), at SNR of 5 dB respectively. According to (15), the increment in \(N\) increases \(\delta_{H}^{2}\). As per (32), a larger value of \(\delta_{H}^{2}\) decreases the \(\ \left(\frac{-y_{SU}}{\delta_{H}^{2}}\right)\). The smaller value of negative exponential increases the \(exp\left(\frac{-y_{SU}}{\delta_{H}^{2}}\right)\). As \(N\) increases \(exp\left(\frac{-y_{SU}}{\delta_{H}^{2}N_{1}}\right)\) and \(exp\left(\frac{-y_{SU}}{\delta_{2}^{2}N_{2}}\right)\) increases. As these two terms are more dominant in (32) than \(\ exp\left(\frac{-y_{SU}}{\delta_{H}^{2}}\right)\), the probability of outage decreases for _SU_. This analysis is repeated for _WUI_ in Figure 5. The probabilities of outage are 0.02921, 0.01471, 0.007384, 0.003699 and 0.001851 for \(N=32\), 64, 128, 256, and 512 at SNR of 5 dB respectively. After substituting \(N_{1}=N_{2}=N/2\) in (15), increment in \(N\) increases \(\delta_{H}^{2}\).