## 5 Numerical Results  
Various numerical results are given to verify how exact the derived expressions are. We match numerical results with the analytical results in the previous sections, and provide insights for the proposed networks. We consider a scenario where the distances from the source to \(U_{1}\) and \(U_{2}\) are 300 m and 100 m, respectively. The pathloss exponent is \(\eta=3\). The density of the interference around each user is \(\lambda_{1}=\lambda_{2}=10^{-5}\) interferer/m\({}^{2}\). The figures in this section are implemented via MATLAB 2017a. It is recommended pre-installing _Statistics and Machine Learning Toolbox_ for the function _poissrd_ which helps construct Poisson point processes, in which their simulation curves are obtained by averaging at least 50,000 iterations. Within each iteration, the position of each interferer, the information channels and the interference channels are reconstructed independently. The position of the interferers affecting \(U_{n}\) for each iteration are uniformly distributed inside a square area with length \(10^{6}\) m around each respective user. Furthermore, the analytical curves are obtained with the help of [39] providing an exceptional algorithm for numerically evaluating the Fox H-function. Other parameters are introduced as follows:  
* The number of transmit antennas, without pointing out, is set default at \(K=2\). * The target data rates of \(U_{1}\) and \(U_{2}\) are \(\bar{R}_{1}=0.5\) (\(\mathrm{bps/Hz}\)) and \(\bar{R}_{2}=1.5\) (\(\mathrm{bps/Hz}\)), respectively. Furthermore, the power allocation are \(\theta_{1}=0.8\) and \(\theta_{2}=0.2\). * The shape parameters of the interference channels affecting \(U_{1}\) and \(U_{2}\) are \(\alpha_{1}=\alpha_{2}=1\), respectively. In Figure 2, three schemes of antenna configuration are plotted to indicate system performance in term of the outage probability versus SIR for different values of \(K\). From Figure 2, at very high SIRs, a significant improvement in outage can be seen. Firstly, we see that our proposed NOMA scheme in BAS mode almost exhibit bigger gap between performance of two users due to different power allocation factors at high SIR. Secondly, outage performance in case of \(K=4\) outperforms that of \(K=2\). It can be observed that the number of selected transmit antenna does not change outage performance at both users. Outage in the BAS mode is better than that in the RAS mode. This result shows that tight matching between Monte-Carlo simulations and analytical simulations. The main result is reported in Figure 3 in term of the system performance, i.e., \(1-(1-OP_{1}^{X})(1-OP_{2}^{X})\) with \(X\in\{RAS,BAS-1,BAS-2\}\). In low SIR regime, the performance of \(U_{1}\) is very poor, thus our concern should focus on improving \(U_{2}\). In high SIR regime, the performance of \(U_{1}\) are much better, thus our target on \(U_{2}\) is not good. As a result, the RAS scheme in the considered NOMA system provides the worse case or upper performance bound. It also can be perceived that in lower SIR regime,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RAS**} & \multicolumn{2}{c}{**BAS-1 (22)**} & \multicolumn{2}{c}{**BAS-2 (28)**} \\ \hline \multirow{2}{*}{\(U_{1}\)} & Sim. & Ana. & Sim. & Ana. & Sim. & Ana. \\ \cline{2-7}  & \(OP_{1}^{RAS}(1)\), (7) & \(F_{G_{1}}(\tau_{1})\), (9) & \(OP_{1}^{RAS}(k_{1}^{*})\), (7) & \(F_{G_{1}}^{(k_{1}^{*})}(\tau_{1})\), (26) & \(OP_{1}^{RAS}(k_{2}^{*})\), (7) & \(F_{G_{1}}(\tau_{1})\), (9) \\  & \(E_{x_{1}}^{RAS}(1)\), (12) & \(E_{x_{1}}^{RAS}(1)\), (17) & \(E_{x_{1}}^{RAS}(k_{1}^{*})\), (12) & \(E_{x_{1}}^{RAS-1}\), (27) & \(E_{x_{1}}^{RAS}(k_{2}^{*})\), (12) & \(E_{x_{1}}^{RAS}(1)\), (17) \\ \hline \multirow{2}{*}{\(U_{2}\)} & [PERSON]. & [PERSON]. & [PERSON]. & [PERSON]. & [PERSON]. & [PERSON].