# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.8 Machine Learning (ML)-Based NOMA Communications  
It is unfeasible to design the number of variables that need to be configured for NOMA-enabled future wireless systems as the complexity of the system model increases. Due to the combinatorial complexity, it is challenging to jointly optimise the many design variables. Therefore, there is a lot of potential for machine learning (ML) to be used in resolving the optimization challenges for NOMA systems that are covered in this study. The applicability of ML and deep learning (DL) for resource optimization issues in IoT and other cellular networks is explored in [162], along with a brief overview of the NOMA systems applications. Next, we deliberate on NOMA combined with ML. Rate optimization in ML based NOMAThe most popular strategy in the NOMA literature for dealing with the challenge of combinatorial complexity is to divide it into a number of smaller problems. As necessary, one or more of these sub-problems can be helped using \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**RIS-NOMA Variant** & **Problem Discussed** & **Variables** & **Results** & **Open Issues** \\ \hline
**Physical Layer** & & & & \\
**Security (PLS)** & & & & \\ \hline
**Channel Estimation** & & & & \\ \hline
**THz Communication** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._ML approaches. For instance, the user selection sub-problem is solved in [163; 164] using an unsupervised clustering approach, while the power allocation issue is dealt with using traditional optimization. The channel assignment is dealt with using a DRL algorithm in [165], which studies a multi-carrier environment, while the power allocation is again dealt with using traditional optimization. Power optimization can also use ML techniques when more variables are introduced to the problem. For instance, the authors of [166] allocated power using an RL algorithm when a deliberate jammer was present. The key point is that ML techniques are capable of handling a portion of these issues and can be utilised in conjunction with conventional methods for optimization when the model complexity increases and introduces more variables, as it is typical for NOMA-enabled systems to have more designed variables for NOMA systems such that the numbers of designed variables becomes too large for ML algorithms as complexity is added via multi-cell and carrier, cooperative, etc. In such circumstances, a deep learning neural network as suggested in [167], for example, may be investigated to determine the parameters that have the greatest influence on the sum-rate performance. Following that, the chosen variables to be optimised are selected using traditional optimization methods or alternative ML techniques. _Quantum ML:_ While using ML in NOMA-enabled systems has many appealing benefits, there are drawbacks as well. The amount of computing power needed to perform some of these ML algorithms is one of the main issues with ML algorithms. To help with this, new trends such as quantum machine learning (QML) are studied for future-generation networks. The authors of [168] focused on how QML may greatly improve the problems in multi-objective optimization that require the adjustment of many constraints and their parameters, which is a typical scenario for NOMA. The huge amount of data needed to apply ML algorithms presents another problem. Today, communications networks gather a lot of data which are of no use, including CSI, user positions, and other information, using them solely for immediate scheduling decisions. These can be used in ML algorithms and then in NOMA-enabled systems, as big data processing develops quickly. _Supervised ML:_ The channel changes so quickly when ML is applied at the physical layer, so there is less time for ML algorithms to gather useful data to learn from. This presents another issue. This makes it particularly difficult to deploy supervised algorithms that gain knowledge from the past at the physical layer.