## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Resource Allocation  
In NOMA, one resource block (RB) is shared by multiple users, and the SIC receiver is used to decode the user information at the receiver end based on the user's channel gains. Interference between the users can be avoided by choosing proper power allocation algorithms. Otherwise, resource allocation issues such as user pairing and power allocation (PA) will arise. In user pairing, the users with less power are allocated with more channel gain, and users with more power are allocated with less channel gain to make channel fairness to all the users at the transmitter end. At the receiver end, the SIC receiver is used to decode the same. In this method, if the number of users increases, then the decoding complexity also increases at the receiver end. This is one of the major problems in user pairing. Along with this, another problem, i.e., if the users with high and low gain are \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Focus Area** & **Research Contribution** \\ \hline
[11] & A comprehensive review of DL-based NOMA & This study surveys DL-based NOMA techniques, benefits, and challenges of DL integration with emerging technologies. It briefly discusses future directions for DL-based NOMA systems. \\ \hline
[19] & DL-based NOMA for 5G networks & It discusses DL-based NOMA models exploited for user detection, signal detection, channel allotment, power allocation and resource allocation. It also highlights the shortcomings and benefits of DL approaches to solve NOMA challenges. \\ \hline
[38] & Performance analysis of DL-based NOMA & It outlines DL-based NOMA, its challenges, and potential benefits. \\ \hline
[37] & Overview of NOMA & It focuses on NOMA’s impact on multi-cell networks. \\ \hline
[36] & Advantages of NOMA & It comprehensively discusses the benefits of NOMA and the integration of cognitive networks, MEC and MIMO. \\ \hline
[40] & DL’s impact on wireless communication & It discusses DL-based sum rate maximization for NOMA \\ \hline
[41] & NOMA in next-generation multiple access & It discusses NOMA as a promising candidate for 6G networks. It also discusses associated research opportunities and future visions. \\ \hline
[42] & DL for downlinking MIMO-NOMA systems & This article surveys DL in SP blocks of downlink MIMO-NOMA systems. It briefly outlines possible future research directions. \\ \hline
[43] & DL-enhanced NOMA transceiver designs for massive MTC & It comprehensively discusses state-of-the-art challenges and future directions for DL-enhanced NOMA systems from the perspectives of online adaptability and reconfigurability toward the ever-changing environment in future mMTC. \\ \hline
[44] & In this study, we comprehensively discuss state-of-the-art, key performance indicators, challenges, and future directions for DL-enhanced NOMA systems. \\ \hline Our Work & DL-based NOMA & It provides a review of the power allocation optimization problem through DL methods. It also outlines various future research directions. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of recent reviews on DL-based NOMA. transformed to mid-gain, then mid-gain users may be paired or may not, which leads to reduced channel capacity. To overcome the user pairing issues, optimization techniques, game theory, machine learning and deep learning algorithms are proposed in the literature. The authors proposed an optimization method while pairing two users [45; 46; 47]. To optimize the user pairing, the channel gain should not be less than the predefined threshold. A strong channel pairing algorithm can increase the system capacity and fairness in user pairing. In [46], the authors used a new pairing concept, i.e., the highest channel gain users are paired with the next highest gain users. Different Game theory algorithms for multiple user pairing and machine learning algorithms for user pairing have been proposed in recent research. In [26], the authors proposed an RL-enabled joint power allocation and user pairing scheme.