## 9 Simulation Results and Discussion  
In Figure 4, the simulation results illustrate the comparison between the proposed DL scheme for channel estimation and conventional NOMA scheme that employs MMSE procedure for estimating the channel parameters. The estimated channel coefficients using both schemes will be used in signal recognition for far, middle, and near users and the simulation results are shown in terms of bit error rate (BER) versus transmitted power. All users in the NOMA cell-based DL channel estimation show sufficient improvement in lowering the bit errors compared to the conventional NOMA scenario, especially when the assigned power is increased. It can be noticed that for certain BER values, such as \(10^{-2}\), the power saving achieved by DL scheme is approximately 5-8 dBm for far and middle users, while for the near user, the power saving is up to 4 dBm. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Tool & MATLAB \\ Modulation type & QPSK \\ Number of Users & 3, [2; 3; 4; 5; 6; 7; 8; 9; 10] \\ System Bandwidth & 1 MHz \\ Fading and channel & (Rayleigh + AWGN), (Rician + AWGN) \\ No. DNN layers & 4 \\ Number of Iterations & \(10^{6}\) \\ Optimizer & ADAM \\ Learning Rate & 0.001 \\ Number of neurons/layer (LSTM) & 300 \\ Number of pilots & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the simulation parameters. Figure 5 demonstrates the results for outage probability metric versus transmitted power for the three examined users in NOMA system on the basis of DL and conventional NOMA channel estimator schemes. Far user and near user simulation results indicate an approximately 5 dBm improvement in power saving to achieve a certain outage probability (\(10^{-3}\)) when DL-based channel estimation scenario is implemented compared to the conventional estimation scheme. Likewise, the middle user with the DL estimation scheme shows more enhancement in power saving compared to the MMSE procedure, by 5-7 dBm approximately, which proves the superiority of the proposed DL estimation scheme. Figure 6 displays the simulation results for the sum rate versus the transmitted energy for the three examined users in the NOMA cell. In this figure, three different channel estimation schemes are inspected, i.e., the proposed DL approach, conventional NOMA based on the MMSE scheme, and the DL algorithm for joint channel estimation and signal detection that was applied in [14]. Based on the simulation results, it can be clearly noticed that the proposed DL channel estimation scheme shows dominance over the conventional NOMA scenario, with 6 b/s/Hz approximately, and also indicates an improvement over the DL algorithm implemented in [14] by 2 b/s/Hz. These results verify the effectiveness of the proposed DL scheme in estimating the channel coefficients before being utilized in the decoding process. Figure 4: BER vs. power-based proposed DL-NOMA and conventional NOMA (Rayleigh Channel). Figure 5: Outage probability vs. power-based DL and conventional NOMA (Rayleigh Channel). Figure 7 illustrates the simulation results for the individual capacity metric for each user in the NOMA cell when both the proposed DL-assisted channel estimation and conventional NOMA-based MMSE channel estimation schemes are employed. As expected, when power level starts to increase, the achieved capacity for the near user shows significant difference by at least 8 b/s/Hz approximately over far and middle users' rates. This may be justified by the good channel condition for the near user compared to other users in the cell. Furthermore, the proposed DL approach still delivers noticeable enhancements with respect to other users, but with little impact especially for the far user, due to interference and weak channel conditions. Figure 8 illustrates the simulation results for BER versus transmitted power when the Rician channel is implemented. The proposed DL channel estimation scheme and conventional MMSE scheme will be further inspected by the Rician channel model.