## 5 Simulations and Discussion
### Simulation Results  
In this section, we provide a detailed description and discussion of the simulation results. Initially, the system was tested under the effect of the LSTM CE without adding the adaptive coding and modulation stages. This set up is indicated as the proposed LSTM NOMA system. To further enhance \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of layers & 4 \\ Number of neurons/layer (Input layer) & 1000 \\ Number of neurons/layer (LSTM) & 200 \\ Number of neurons/layer (fully connected layer) & 6 \\ Learning rates & 0.005, 0.0001 \\ Length of training data & \(1333\times 1\) \\ Length of testing data & \(666\times 1\) \\ Size of data & \(2000\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LSTM channel estimation (CE) simulation parameters. Figure 8: Loss performance of the proposed LSTM network during the training phase. Figure 7: Root-mean-square error (RMSE) performance of the proposed LSTM network during the training phase. the BER as well as the bandwidth consumption, adaptive BCH codes, constellation rotation, and cyclic Q-delay were added to the system. This system configuration is indicated as the proposed adaptive LSTM NOMA system. The results of the CE and the SNR values obtained in the initial step were used to reallocate the power coefficients and select the appropriate code rates for each user. The overall BER of the proposed adaptive system was evaluated and plotted against the SNR. Figure 9 shows the BER versus the SNR for the proposed PD-NOMA scheme versus the conventional NOMA system and the proposed LSTM-NOMA system without applying the adaptive coding and modulation stages. The results show that without implementing the constellation rotation and the cyclic Q-Delay, the BER performance reached a minimum of 5% and a maximum of 50% lower BER than that of the conventional NOMA system at SNR values ranging from \(-2\) to \(12\) dB. In addition, when the adaptive coding and modulation stages were added to the system, the difference reached a minimum of 12% and a maximum of 73% lower BER for the same SNR values. The results verify that supplementing the adaptive coding and modulation stages to the proposed LSTM-NOMA system improves the BER performance and reduces transmission errors when transmitted through a fading channel. The outage probability of the proposed system was computed against the SNR and is illustrated in Figure 10.  
The outage probability for the proposed NOMA system was compared to the conventional NOMA system, and the simulations showed that the proposed system reached an improved outage probability performance for the SNR range of \(-15\) to \(-2\) dB, with a maximum improvement of 10% corresponding to SNR values in the range of \(-14\) to \(-4\) dB. The resulting outage probability for the proposed system was lower than that of the conventional PD-NOMA system as a result of applying LSTM channel estimation, which resulted in a more accurate channel estimate and therefore delivered the specific QoS requirements to the users. Finally, the user and sum rates of the proposed system were evaluated for two users and compared to the conventional NOMA system. The plot of the user rates against SNR, shown in Figure 11, indicates that user 1 gained a 33% average increase in the rate, while user 2 gained a 10% average increase in the rate at SNR values  
Figure 9: Bit error rate (BER) against the signal-to-noise ratio (SNR) for the proposed system versus the conventional NOMA and LSTM NOMA. between 9 and 19 dB, when compared to the conventional NOMA system. These enhanced user rates indicate that using the LSTM guaranteed that the users are precisely able to determine the best transmission data rate according to their estimated channel condition. Figure 12 shows that the sum rate of the system increased by an average of 37% at SNR values ranging from 10 to 19 dB when the proposed scheme was applied.