## 5 Numerical Result  
In this section, we provide illustrative numerical results to evaluate the performance of the proposed network in terms of OP by using a Monte Carlo simulation. The main parameters were set as \(\beta=0.2\), \(d_{SR}=10\) m, \(\eta=0.7\), \(d_{PS}=10\) m, \(\alpha_{F}=0.85\), \(d_{PR}=10\) m, \(\alpha_{N}=0.15\), \(d_{RF}=20\) m, \(d_{RN}=15\) m, \(R_{N}=0.01\), \(R_{F}=0.01\), \(N_{0}=-94\) [dBm], \(\varepsilon=0.95\) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Value** & **Input** & **Value** \\ \hline \(\beta\) & 0.2 & \(d_{SR}\) & [10,20] [m] \\ \(\eta\) & 0.7 & \(d_{PS}\) & [10,20] [m] \\ \(\alpha_{F}\) & 0.85 & \(d_{PR}\) & [10,20] [m] \\ \(\alpha_{N}\) & 0.15 & \(d_{RF}\) & [20,40] [m] \\ \(R_{F}\) & [0.01, 0.02] & \(d_{RN}\) & [15,30] [m] \\ \(R_{N}\) & [0.01, 0.02] & \(N_{0}\) & \(-94\) [dBm] \\ \(\varepsilon\) & 0.95 & \(P_{PB}\) & [\(-30\),30] [dBm] \\ \(\ell\) & 2.7 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of the DNN. Figure 3: The operational design of DNN for the system. and \(\ell=2.7\), except for the special case. Furthermore, Ana., Sim., and Asymp. are the abbreviations for analytical, simulation, and asymptotic, respectively. For the DNN, we set five hidden layers with each layer having 128 neurons, which was implemented in Python 3.11.4 using Keras 2.8.0 and TensorFlow 2.8.0, with 100 epochs for training the DNN. In Figure 4, we utilized the MSE of the training and the validation to evaluate the accuracy of the proposed DNN. As can be seen, the MSE converged to a value lower than \(10^{-5}\) after 40 epochs. Figure 5 plots the OP of two users (FU and NU) versus \(P_{PB}\) in dBm while varying the target rate \(R_{F}=R_{N}\). It can be observed that over the whole range of \(P_{PB}\) in dBm, the OP curves and analytical results agreed exceptionally well based on the Monte Carlo simulation. Furthermore, the asymptotic findings produced in (23) and (24) were extremely valid with the theoretical analysis ones at high \(P_{PB}\), confirming the accuracy of our developed analysis approach. In addition, we can observe that the outage performance was improved when increasing the transmit power but did not change in high \(P_{PB}\). The fact that the OP depended on more factors than only transmitting \(P_{PB}\) can be shown in Remark 1 and 2. Another observation is that the excellent agreement between the simulation and DNN prediction outcomes validated our proposed methods. Figure 4: The MSE for DNN training and validation. Figure 5: The outage probability versus \(P_{PB}\) in dBm when varying the target rate \(R_{F}=R_{N}\). Figure 6 depicts the OP of users versus \(P_{PB}\) in dBm with varying \(\varepsilon\). As shown in Figure 6, we can observe that the OP increased when \(\beta\) decreased and converge with a large \(P_{PB}\). This is due to the OP depending on the interference channels in the SINR of two users when decoding the signal. In Figure 7, we plotted the OP versus \(\alpha_{F}\) when varying \(P_{PB}\) in dBm. First, we can observe that the OP of FU decreased when \(\alpha_{F}\) increased. The reason was that when \(\alpha_{F}\) increased, the transmit power of FU was greater than that of NU. Furthermore, the OP of NOMA was better that OMA in the range \(0.5<\beta<0.8\). In addition, there existed an optimal point \(\alpha_{F}\) for the OP of two users. Specifically, when \(P_{PB}=-10\) dBm the optimal point for the OP of two users was \(\alpha_{F}=0.64\), \(\alpha_{F}=0.59\) for \(P_{PB}=0\) dBm, and \(\alpha_{F}=0.54\) for \(P_{PB}=10\) dBm. Figure 8 shows the OP of two users versus \(\eta\) when varying \(P_{PB}\). It can be observed that the OP was improved when increasing the energy efficiency \(\eta\). This is due to the energy efficiency \(\eta\) being larger, leading to a greater transmit power of PB in (4) and (5). Figure 9 illustrates the OP of two users versus \(\beta\) when varying \(P_{PB}\).