## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### User Fairness  
Several recent studies [82; 83] have discussed the benefits of employing NOMA. This increases system throughput, spectrum efficiency and user fairness. We can also obtain an extremely highly reliable connection. Time and frequency resources are distributed to users in the spatial domain through the power domain or code domain NOMA [79]. A recent work [82] uses a deep learning algorithm to ensure user fairness by dividing users into low-rate and high-rate requirement users considering their mobile phone usage habits. The authors consider a NOMA system with DL-based coordinated multi-point (CoMP), used in 5G cellular networks to guarantee the rate requirements from the different edge users. Figure 4 evaluates the performance of user sum rates in dynamic point selection CoMP (DPS-CoMP) subchannels and the number of cells subchannels. Figure 4 shows that the user sum rate in the DPS-CoMP subchannel of each cell in the DPA algorithm, the NOMA-CoMP algorithm, and the maximum throughput (MT) algorithm all increase as the number of subchannels increases. In the NOMA power domain, power distribution among users varies depending on the channel characteristics and user-specific channel quality. As a result, consumers located far from the BS will receive more power, and vice versa. According to the works cited in [84], fairness for NOMA in 5G is highlighted by the fact that, in downlink mm-wave NOMA, various data from all users is thus overlaid in the power domain at the transmitter, and the SIC is performed at the receiving side. By integrating SIC and superposition coding (SPC) at the transmitter end and receiver side, respectively, we may use NOMA to increase spectral efficiency. The max-min fairness of both the average CSI and instantaneous CSI is also discussed in this literature. Considering the interaction between NOMA and cooperative transmission, the integration of NOMA with several emerging 5G technologies, the correlation with other NOMA variants, and the resource control of NOMA, the authors in [85] focus on state of the art in power-domain multiplexing-based NOMA. Considering the hybrid beamforming system described in the article [85], which employs phase shifters and sets of switches, down-converter, LNA, ADC, and DAC are components of the radio frequency (RF) chain. The price of the system rises along with the number of RF chains. To decrease the number of RF chains, the hybrid beamforming approach is applied, and the system's price will be reduced as a result. 5G deep learning systems have been researched in the literature. Power allocation, DoA estimation [86], physical layer security [87], channel estimation [88], energy optimization, etc., are all included in the program, which significantly addresses user fairness issues in NOMA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Context** & [77] & [78] & [79] & [80] & [81] & [76] \\ \hline CR-NOMA & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Downlink & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Uplink & - & - & - & - & - & - \\ \hline Imperfect SIC & Yes & Yes & Yes & - & - & Yes \\ \hline Imperfect CSI & - & - & - & - & Yes & Yes \\ \hline OP analysis & Yes & - & Yes & Yes & Yes & Yes \\ \hline Throughput & - & Yes & - & Yes & - & Yes \\ \hline \hline \end{tabular}  
* \({}^{*}\)-\({}^{*}\) means this feature has not been considered in those references.  
\end{table}
Table 3: Comparison of studies reported on SCI/CSI in NOMA [76].