## 4 Result and Performance Analysis  
The NOMA system design was investigated using MATLAB. The simulation parameters are summarised in Table 3.  
Figure 2 shows the trade-offs between throughput maximisation and system fairness with 4 to 24 users. The proposed algorithm, i.e., ILPSO, outperformed GOS by 17%, MWF by 21.9%, and orthogonal frequency-division multiple access (OFDMA) by 33% [14]. As observed in Figure 2, ILPSO Scenarios 1 and 2 outperformed Scenario 3, along with GOS, MWF, and OFDMA. These two scenarios achieved higher throughput because no minimum data rate constraint was imposed during the optimisation process. However, ILPSO Scenario 3 still achieved a higher data rate than the other schemes whilst also considering fairness and data rate constraint. Considering that system design in ILPSO Scenario 3 assigned higher priority to fairness, it also exhibited higher throughput than GOS, MWF, or OFDMA. The system design applied a penalty during power optimisation, restricting the power ratio, user data rate, and fairness to prevent constraint violation. ILPSO Scenario 3 is the preferred scenario because of the penalty and fairness weight applied to the function. The objective function was evaluated for the three ILPSO scenarios that achieved better performance than the existing approaches. ILP was performed earlier as a pairing scheme to relax the optimisation process. Hence, PSO was applied to two variables restricted by optimisation penalty to prevent the power constraint from reaching the upper and lower power bounds. The penalty function and scaled weight were used in the objective function to assist the algorithm in achieving the highest throughput during the optimisation. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Bandwidth (BW) & 5 MHz \\ Cell radius & 200 m \\ Maximum transmit power (Bs) & 30 dbm (1 W) \\ Noise power spectral density (\(N\).) & \(-174\) dBm/Hz \\ User minimum data rate & 500 b/s \\ Number of transmission antennas & 1 \\ Noise figure & 9 dBm \\ Shadow standard deviation & 8 dB \\ Throughput calculation & Shannon’s capacity \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the system design. Moreover, the PSO algorithm achieved a good solution in earlier iterations and escaped the local optimal search space. Considering that reasonable solutions were found after a few iterations, an exploration can identify all possible solutions, enhancing the search for a better solution. Therefore, PSO provides acceptable solutions for power allocation that maximise throughput. In Figure 3, \(J\)\(I\)\(I\) is evaluated for 4 to 24 users. The ILPSO scenarios are compared with GOS [17], MWF [14], and OFDMA. ILPSO Scenario 3 assigns higher priority to system fairness, as indicated by its highest fairness value, because more weight is assigned to making system fairness a priority in the objective function. ILPSO Scenarios 1 and 2 exhibit lower fairness (below 0.5 for 12 users) due to constraint considerations, i.e., fairness weight is not considered. The ILPSO scenarios achieve better fairness for a higher number of users compared with GOS and OFDMA. ILPSO Scenario 3 is applied to ensure that system will have a minimum user data rate and fairness. It demonstrates greater fairness due to the fixed penalty used in the problem formulation. ILPSO Scenarios 1 and 2 present lower fairness values due to violation of the constraints. However, ILPSO Scenario 2 shows better fairness than ILPSO Scenario 2 because of the minimum data rate constraint applied to the objective function. In Figure 4, user throughput for the number of channels is estimated with the number of users on the basis of the high and low channel gains. The primary concern is to ensure that a low channel gain user is still achieving a sufficient data rate whilst a high channel gain user is always achieving a better data rate. In ILPSO Scenario 3, a higher user data rate is observed for a low channel gain user than in ILPSO Scenarios 1 and 2.