## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Power Allocation  
One crucial challenge is how to allocate power when there are limited resources to make the most of the benefits of the NOMA system. It has been established that this issue of optimum power allocation is NP-hard, indicating that it is impractical and expensive to study all possible channel assignments to find an ideal solution. As a result, several methods have been put forth by researchers to deal with this issue. Solutions include distributing power for a downlink single input and single output (SISO) NOMA system [48], distributing power for the most equitable distribution of users [49], and distributing power for the most energy-efficient use of resources [50]. Deep learning techniques must be used because several solutions have been demonstrated to be less than ideal. A thorough literature assessment of deep learning-based approaches to the power allocation concern will be provided in sufficient depth in this section. Utilizing DL in NOMA, deep neural network generic architecture efforts are at the forefront of current technological advancements in power allocation. To distribute power to consumers in the best possible way, [35] suggests a deep reinforcement learning (DRL) method; specifically, an artificial neural network (ANN) is employed to perform channel assignment. The system model is based on [PERSON] and several users in a downlink NOMA scenario. Users serve as the performance environment for the deep learning algorithm, which treats [PERSON] as an agent. To allocate resources and channels to users, [PERSON] first chooses a task (channel assignment) from a set. A feedback signal is then provided towards the BS to help assign users in the following transmission based on the users' responses. The three crucial parts of this process are the status space, action space, and reward function. The channel information is responsible for the state space. The agent (BS) chooses a single channel for data transmission for a single user in the action space. The collection of actions is constrained to meet the requirements of user channel allocation, so each user is associated with a unique action. After the user acts, the allocation procedure is complete. The signal returned to the BS as a result of a failed or successful transmission at the conclusion of each time slot is the reward function, as shown in Figure 3. The data rates each user experiences and is sent by the BS to make up the signal. The goal of [51] is to maximize this incentive signal and, in turn, optimize each user's data rate. The acquired findings provide a sum-rate comparison of Joint Resource Allocation (JRA) without downlink versus JRA with DL, where the non-DL variant is significantly outperformed by the DL counterpart. [52] suggests a power allocation plan that uses DL approaches to optimize the system sum rate in a downlink NOMA environment with an incomplete SIC. The algorithm for finding the best power allocation is exhaustive. In a recent study [44], a power allocation approach for imperfect SIC to enhance the experienced system sum rate is suggested. The suggested technique extensively uses deep learning to predict the ideal power allocation criteria. We have summarized the research contributions on DL-based power allocation methods in NOMA in Table 2.