## 4 Results and Discussion  
Numerical analysis by performing Monte Carlo simulations was performed in this section to verify the accuracy of the proposed algorithm. To perform simulations, we used MATLAB tools. The overall simulation setup is summarized in Table 2; the values were  
Figure 4: (**a**) Clustered PD-NOMA, (**b**) direct PD-NOMA, and cooperative PD-NOMA within the clusters. selected based on the 3 GPP standards. Simulations were performed on SBS-associated users considering both indoor and outdoor small cell users. All active SBS nodes were randomly deployed using a Poisson random distribution within a macro cell area, giving the targeted region's coverage probability as shown in Figure 5. The power coefficient '\(\alpha\)' range was from \(0\leq 1\), and the simulations were performed to evaluate and give a comparative system capacity with the implementation of OFDMA, PD-NOMA, and the proposed clustered PD-NOMA techniques. Figure 6 shows the channel capacity comparison by comparing the user rate of u1 and u2 achieved with unified PD-NOMA and clustered PD-NOMA in Mbps. It can be seen in the results that the region covered by clustered PD-NOMA was considerably more significant than the unified PD-NOMA scheme due to the increased channel gain difference achieved with the clustered PD-NOMA. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Sub Carrier Bandwidth & 5 MHz \\ \hline System Bandwidth (W) & 10 MHz \\ \hline Transmission Power of Pico BSs (HSBS) & 25 dBm \\ \hline Transmission Power of Femto BSs (LSBS) & 10 dBm \\ \hline Channel Gain (MBS) & 14 dBi \\ \hline Channel Gain (SBS) & 7 dBi \\ \hline Indoor/Outdoor Path Loss Coefficient & 2 \\ \hline Radius of MBS (Macro BS) & 500 m \\ \hline Radius of HSBS (Pico BS) & 25 m \\ \hline Radius of LSBS (Femto BS) & 10 m \\ \hline No. of HSBS & 10 \\ \hline No. of LSBS & 30 \\ \hline No. of Users & 2–60 \\ \hline No. of Sub-Carriers NOMA & 5 \\ \hline AWGN & 169 dBm/Hz \\ \hline Fading & Rayleigh \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 5: Voronoi tessellation of randomly deployed users under Poisson distribution. Figure 7 shows the throughput gain achieved with the increased number of users associated per cell; it can be seen that 4% more throughput gain was achieved with clustered PD-NOMA (without cooperative PD-NOMA) than the unified PD-NOMA scheme. In addition, it can be analyzed from the results that the throughput gain achieved with unified PD-NOMA was 11% improved as that achieved with the conventional OFDMA scheme, and it was enhanced by 15% with the clustered PD-NOMA as compared to the traditional PD-NOMA scheme. Figure 6: Channel capacity comparison of unified PD-NOMA and clustered PD-NOMA scheme. Figure 7: Number of associated users with the SBS versus the maximum throughput achieved. After implementing the cooperative method on clustered PD-NOMA, the throughput further improved. Figure 8 shows the comparative analysis of throughput gain achieved with the proposed clustered PD-NOMA with cooperative nodes, the throughput achieved with the IMHC scheme [1], and the results achieved with unified PD-NOMA and OFDMA techniques. With the proposed scheme, the throughput gain improved by 23, 29, and almost 65% as compared to the unified PD-NOMA scheme, IMHC clustering scheme, and the conventional OFDMA scheme, respectively. Results showed that the throughput efficiency improved remarkably for a varied number of users when implemented with the proposed clustered PD-NOMA scheme, whereas the number of users associated per cluster would be based on maximum channel gain difference between the associated users for the clustered PD-NOMA. The throughput gain improved with the clustered PD-NOMA due to low interference values and cooperative user implementation within the PD-NOMA clusters, as interference was mitigated by applying SIC, and as the clusters were formed, the distance of users from the SBS also decreased; therefore, the inter-cluster interference also decreased.