## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Channel State Information
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Technique** & **Model** & **System Description** & **Model Limitations** & **Research Contribution** \\ \hline \multirow{4}{*}{[53]} & \multirow{4}{*}{Reinforcement} & \multirow{4}{*}{\begin{tabular}{c} Special Neural \\ Network \\ \end{tabular} } & \begin{tabular}{c} Single BS using \\ cooperative \\ reinforcement \\ learning algorithm \\ for adaptive power \\ allocation in D2D \\ communication \\ \end{tabular} & It considers a & \begin{tabular}{c} Considering the OFDM \\ system without NOMA, \\ maximize overall SU \\ interference while \\ keeping data rate and \\ power. \\ \end{tabular} \\ \hline \multirow{4}{*}{[54]} & \multirow{4}{*}{Reinforcement} & \multirow{4}{*}{Q-learning} & Dynamic & Computationally \\  & & multichannel access & expensive, DQN is \\  & & problem, where & not easy to tune, and \\  & & multiple correlated & more realistic and \\  & & channels consider an & complicated \\  & & unknown joint & scenarios such as \\  & & Markov model & multi-user systems \\  & & & are not considered. \\ \hline \multirow{4}{*}{[55]} & \multirow{4}{*}{Supervised} & \multirow{4}{*}{CNN} & The power control & \multirow{4}{*}{\begin{tabular}{c} Compared with \\ DNN, it still lacks \\ fitting ability. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Maximization of \\ throughput by \\ configuring the power. \\ \end{tabular} } \\  & & & & \\ \cline{1-1}  & & & DNN-based & \multirow{4}{*}{\begin{tabular}{c} Considering an underlay \\ cognitive radio network, \\ it focuses on maximizing \\ spectral efficiency. \\ \end{tabular} } \\  & & & & \\ \cline{1-1}  & \multirow{4}{*}{Reinforcement} & Two DNNs & \multirow{4}{*}{\
\begin{tabular}{c} Sensitivity to \\ connectivity fully \\ channels without \\ interference from the \\ primary user. \\ \end{tabular} } &
\begin{tabular}{c} This system does not \\ consider an optimal \\ DNN structure. \\ \end{tabular} & Keeping the interference of primary users below \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Nevertheless, nonlinear reconstruction techniques are inevitable since the channel sparsity trends have been frequently taken for granted in previous studies. Therefore, super-resolution direct arrival (DOA) estimates, and signal identification cannot be accomplished using standard approaches since they are inefficient and unreliable. The NOMA system has recently been enhanced with a promising machine learning (ML) approach to enable the auto-detection of the CSI. The DL [63] idea, introduced in 2006 and a typical branch of machine learning, is a particularly effective technique for managing large amounts of data and resolving challenging nonlinear issues. A few earlier papers [64; 65; 66; 67] included DL in communication in relation to the physical layer, channel coding, and MIMO. The intriguing system that incorporates the DL into the OFDM context has been demonstrated in [68], and its outstanding performance in the context of signal recognition and channel estimation has been confirmed. DL has also been used in traffic monitoring systems, which work admirably [69; 70; 71]. Additionally, DL-based communication systems have shown certain benefits in terms of security, BER, and throughput performance.