## 9 Simulation Results and Discussion
Figure 6: Sum rate vs. power for conventional NOMA, joint DL-NOMA, and proposed DL-NOMA (Rayleigh Channel). Rician fading is a stochastic model for radio propagation, where the signal arrives at the receiver by several different paths, and hence, exhibits multipath interference. Rician fading occurs when one of the paths, typically a line of sight (LOS) signal or some strong reflection signals, is much stronger than the others. A Rician fading channel can be described by two parameters The first one is the Rician factor \(K\) defined as the ratio of the signal power in the line-of-sight component to the scattered power in other components. The other main parameter is \(\Omega\), which represents the total power from both paths and acts as a scaling factor to the distribution. In our simulation file for the Rician channel, we assign \(\mathrm{K}=10\), sample rate = 9600 Hz, and maximum doppler shift = 100. In Figure 8, simulation outcomes for near and middle users indicate a noticeable improvement in lowering the bit errors when the proposed DL scheme is applied compared to the conventional MMSE scenario. The near user shows a substantial improvement in terms of power saving due to  
Figure 8: BER vs. power-based DL and conventional NOMA (Rician Channel). Figure 7: Individual capacity vs. power-for proposed DL-NOMA and conventional NOMA (Rayleigh Channel). the relaxed channel conditions and elimination of interference by the SIC method. In the far user situation, the impact of DL in tracking the channel parameters is limited due to the interference and weak channel conditions. Figure 9 demonstrates the simulation results for outage probability metric versus power transmitted when the Rician channel is considered, and both DL and MMSE channel estimation schemes are inspected. Far and middle users' simulation results show an improvement within approximately 4 dBm of power saving to achieve a certain outage probability when the DL-based channel estimation scenario is conducted compared to the MMSE scheme. In terms of the near user, the simulation outcomes indicate that the proposed DL channel estimation scheme starts showing improvement regarding the outage probability when the power allocated to the near user is more than 4 dBm, which also proves the dominance of the proposed DL method. In Figure 10, the simulation results regarding the individual capacity for each user are illustrated when the Rician channel model is employed, and both the DL-assisted channel estimation and conventional NOMA based on the MMSE channel estimation schemes are applied. It is worth mentioning that for both far and middle users, DL-based channel estimation shows comparable capacity compared to the MMSE scheme, which can be justified, as the DL scheme is not sufficient enough to mitigate the interference and weak channel conditions for far and middle users in the Rician channel. On the other hand, comparable to the Rayleigh fading results, the achieved capacity for the near user shows a significant difference by at least 6 b/s/Hz compared to far and middle users for the same applied power level. This enhancement in capacity can be justified by the line of site component between transmitter and receiver in the Rician channel and the relaxed fading channel between the near user and BS. Figure 11 illustrates the simulation results for the sum rate versus the number of users examined in the NOMA cell when the Rayleigh channel model is implemented. In this figure and as a benchmark comparison, we have conducted the simulation environment related to the work in [14], which implements the DL algorithm based on joint channel estimation and signal detection as a one-shot process. As indicated from the figure, our proposed DL-based channel estimation scheme achieves a substantial higher sum rate compared to both the conventional NOMA scheme based on the MMSE procedure, and the DL scheme for joint channel estimation and signal detection discussed in [14].