# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 6 Performance Analysis
### Simulation Results  
The network capacity curve as the number of users in a cell increases from 10 to 60 is shown in Figure 3. It can be seen that as the number of users grows, the cell system's capacity grows as well. We can see from Figure 3 that the proposed USFMA offers the highest system capacity for the NOMA system. When there are 40 users, the USFMA outperforms the USMA by 13.11% and the CSS-PA by 40.27%. Furthermore, the USFMA outperforms the OFDMA by 77.72%; this can be justified by the capability of the OFDMA, which can only use one user per sub-channel. As a result, the BS is unable to utilize the spectrum resources to their fullest potential. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Parameter Value** \\ \hline Cell radius & 500 m \\ Minimum distance between BS and UEs & 50 m \\ Minimum distance between two users & 40 m \\ System bandwidth & 5 MHz \\ Maximum number of UTs & 60 \\ Fixed circuit power [47] & 1 W \\ Noise power spectral density & \(-174\) dBm\(/\)Hz \\ Difference tolerance in Algorithm 2 & 0.01 \\ Compensation matrix attenuation coefficient & 0.4 \\ Base station peak power \(P_{BS}\) & 41 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The list of simulation parameters. Figure 4 depicts the network energy efficiency as a function of \(P_{\text{BS}}\). As predicted, The figure shows that when the power of the BS is increased, the system's capacity also grows, with the USFMA giving the best performance. The energy efficiency equation shows that this curve trend is identical to the sum rate curve trend. We can see from Figure 5 that all NOMA system schemes outperform the OFDMA scheme. Furthermore, the energy efficiency of the USFMA is the best through our proposed sub-channel and power allocation using DC programming. When there are 40 users, the USFMA outperforms the USMA by 12.47%, the CSS-PA by 32.43%, and the OFDMA by 75.19%. Figure 4: Capacity of the system vs. BS power. Figure 3: Capacity of the system versus different number of users. The energy efficiency of the system affected by base station power growth with a fixed number of users (\(U=10\)) is shown in Figure 6. We set the power to between one and twelve watts. We can observe in Figure 6 that there is almost a linear relation between system energy efficiency and BS power, and the energy deficiency is inversely proportional to BS power also. All NOMA methods outperform the OFDM system, with the USFMA showing the best performance using DC for sub-channel power allocation. The impact of a fixed circuit to base station power ratio \(\left(\frac{P_{fixed}}{P_{BS}}\right)\) on the system energy efficiency is shown in Figure 7. From the figure, we can notice that the relationship is inversely proportional, i.e., as the power ratio increases, the system energy efficiency decreases. The system is less energy efficient when \(P_{fixed}\) increases and BS power is fixed at 12 watts. However, the NOMA systems, particularly the USFMA, still outperform the OFDMA system when using the recommended resource allocation algorithms. Figure 5: Energy efficiency of the system versus the number of users. Figure 6: Energy efficiency of the system versus BS power. The effect of implementing DC programming alongside the USFMA was studied in both Figures 8 and 9, and the results were compared with USFMA implementing fixed power allocation (FPA). In Figure 8, it can be seen that sum rate for both USFMA-DC and USFMA-FPA is better than OFDMA, with the former showing the best results. Furthermore, when comparing the system's energy efficiency vs. the number of users, it can be seen, as shown in Figure 9, that USFMA-DC provides the best results. For example, if there are 30 users, DC programming has a 1.8% higher energy efficiency than FPA. These results show that DC programming will provide further improvements to the optimized USFMA as discussed above. Figure 8: Sum rate of the system versus BS power. Figure 7: Energy efficiency of the system versus \(\frac{P_{fixed}}{P_{\text{bs}}}\).