## 4 Integration of DL-Based NOMA with Emerging Technologies
### OFDM-Based NOMA  
Deep learning applications in error correction codes and signal processing [142] tend to involve polar code decoding [143], an auto-encoder in the physical layer [144], an auto-encoder for sparse code multiple access (SCMA) coding, and decoding [145], channel estimation and signal detection in OFDM [68], and an auto-encoder for data detection in NOMA [57]. After training, the deep learning-based technique has a computation time between 5 and 11 times faster than non-deep learning alternatives [145]. A recent study [142] compares radio resource allocation (time, subcarrier, power resource, etc.) of previous research in this field utilizing deep learning. Unsupervised learning, for example, using an auto-encoder in the physical layer [67], SCMA [145], and data detection in NOMA [57], in which the input and output of the network are the same, is not suitable for radio allocating resources. Previous radio resource allocation (subcarrier/time slot assignment) research has all focused on single-label classification issues, in which only one subcarrier (channel) may be allocated (labeled) to a single user. However, using NOMA, one subcarrier (channel) can be assigned (labeled) to two users. Standard DNNs are insufficient for multi-label classification. To circumvent this, assume all class combos to be different classes. This results in a combinatorial increase in the number of classes. Overtraining occurs when multiple parameters are trained on a limited training dataset [146]. As a result, new approaches for specific applications must be presented. Smart meter research [146], the multi-speaker direction of arrival estimate [147] and offloading for mobile edge computing [148] are all related publications. Researchers do not, though, apply deep-learning-based multi-label classification in NOMA systems for radio resource allocation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \
\begin{tabular}{c} **NOMA** \\ **Incorporation** \\ \end{tabular} & **Number of Users** & **Offloading Mode** &
\begin{tabular}{c} **DL-Based** \\ **Algorithm** \\ \end{tabular} & **Key Potential** \\ \hline
[134] & Yes & Single user & Partial & Yes &
\begin{tabular}{c} It gives better performance as \\ compared to \\ FDMA-based MA-MEC \\ \end{tabular} \\ \hline
[137] & Yes & Multi-user & Partial & Yes &
\begin{tabular}{c} It speeds up convergence for \\ better solutions \\ than conventional \\ optimization approaches \\ \end{tabular} \\ \hline
[138] & Yes & Single user & Partial & Yes &
\begin{tabular}{c} The DRL-based algorithm can \\ achieve the near-optimal \\ offloading solution itself astigr \\ enough learning \\ \end{tabular} \\ \hline
[139] & Yes & Multi-user & Binary & No &
\begin{tabular}{c} It increases the number of \\ users to offload tasks and \\ reduces the users’ average \\ offloading delay \\ \end{tabular} \\ \hline
[140] & Yes & Multi-user & Binary-Partial & No &
\begin{tabular}{c} It outperforms the binary \\ computation offloading mode, \\ and NOMA outperforms \\ TDMA in the context of \\ computation efficiency \\ \end{tabular} \\ \hline
[141] & No & Multi-user & Binary & Yes &
\begin{tabular}{c} It can achieve near-optimal \\ performance while \\ substantially reducing the \\ computation time by more \\ than an order of magnitude \\ than existing \\ optimization techniques \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of studies on MEC scenario [137].