# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.8 Machine Learning (ML)-Based NOMA Communications
Supervised learning techniques, however, have been investigated for estimation and feedback of the channel, MIMO detection, and other associated issues in mMIMO systems [169; 170]. In what follows, we pay particular attention to the ML methods of unsupervised learning, reinforcement learning, and deep learning. These are used to solve rate optimization issues in the NOMA-based system models examined in this study. These elaborations present a number of potential directions for further research into the use of ML in NOMA-enabled systems. _Unsupervised ML:_ Unsupervised machine learning techniques do not use historical training data. Because of the sub-problem of selecting different users, clustering techniques in particular are suitable for NOMA-based systems. As we previously noted, the standard method in NOMA-related work is to partition the problem into numerous sub-problems because of the combinatorial difficulty of the combined optimization of a significant number of design variables. One of the first sub-problems that academics typically address is user clustering or user pairing. This sub-problem can be solved using clustering algorithms such as K-means clustering. The authors of [163] investigated a mmWave-NOMA system and made efficient use of K-means clustering by taking advantage [164] of the high correlation between user channels and the dominance of the LoS path in mmWave propagation. K-means clustering is useful in system models where an LoS path dominates because it simplifies the task of locating spatially connected users. _Deep Learning (DL):_ Deep learning (DL), which incorporates numerous layers and may collect the features from the source data before executing tasks such as classification, is a more powerful type of machine learning [171]. It is challenging to put a DL method into practice in NOMA-enabled systems due to the fast-changing nature of the physical channel. In NOMA-based systems, deep learning capabilities can still be used in a number of different ways. In [172], a deep recurrent neural network was built to quickly deliver convergence with less computational complexity resource allocation solutions for the NOMA heterogeneous IoT. When the number of objectives and variables becomes significantly high, a neural networks such as those explained by the authors of [167] are effective for obtaining crucial factors. However, when combined with reinforcement learning, using deep learning to optimise the rate for NOMA systems shows the most promise, because the agent uses a multi-layered neural network to make judgments in a practical environment. Deep reinforcement learning (DRL) is the name given to this method to emphasize the integration of DL and RL, and is discussed next. _Deep reinforcement learning (DRL):_ In next-generation wireless communications systems, DRL algorithms are studied with respect to a number of resource allocation issues [173]. These concepts are readily applicable to the NOMA systems examined in this study. In [165; 166], DRL agents can be utilised in NOMA systems for allocating the power level and assigning their corresponding channels. A BS, an end user, or a UAV can be a DRL agent. These nodes must be able to make decisions on their own based on interactions with other nodes in the system. DRL algorithms are also capable of optimising the sum rate of sub-problems, similar to how ML clustering was used for the selection of users sub-problems. Relay and spectrum selection in relay-based NOMA networks and in UAV-NOMA networks are directions using DRL that are possibly worthy of exploration. In this approach, DRL can be used to supplement NOMA-specific optimization algorithms in the NOMA networks. For systems using a hybrid NOMA and MU-MIMO method, such as [174], an RL algorithm can be utilised to solve another interesting problem. _Online learning:_ To solve the issue of NOMA network flexibility, i.e., adopting a new user without having much overhead, online learning is a crucial class of ML algorithms.