## 1 Introduction
### Motivations and Contributions  
The work in [17] shows that the machine learning algorithms have started making inroads into UC optimization for NOMA systems with the availability of huge training datasets generated by [15]. Even though the ANN-based UC [17] can outperform other existing UC schemes in different scenarios, the near-optimal throughput performance comparable to that achieved by the BF-S method is still far beyond reach. Due to the lack of hyper-parameter optimization and the number of hidden layers, the ANN model cannot fully characterize the nonlinear relationship between the UC with the channel diversity and power difference among the users. To precisely model the nonlinearity between cluster formation, channel gains, and power allocation, a deep learning [18] approach can be adopted. As compared to ANN, the deep neural network (DNN) offers more room for optimization. The application of DNN in wireless communications is not new, it can be traced back to 2017 where the DNN method is incorporated into the orthogonal frequency division multiplexing (OFDM) system [19] for signal detection in wireless communications. Additionally, DNN was also implemented in traffic control systems, whose exceptional performance has been proven in [20; 21; 22; 23]. An effective DNN-aided NOMA system of randomly deployed users has been proposed in [24] to model the channel state information (CSI) to estimate the characteristics of the channel automatically. Meanwhile, DNN that automatically analyses the CSI of the communication system and detects the original transmit sequences has been proposed in [25]. The proposed scheme aims to combine the channel estimation process with the recovery of the desired signal suffering from channel distortion and multiuser signal superposition. In [26], a deep learning-based resource allocation framework was developed for the downlink of simultaneous wireless information and power transfer (SWIPT) enabled MC-NOMA system with the pattern division multiple access (PDMA) scheme. The work demonstrates that the deep learning model could substantially reduce the required computation time while attaining a power consumption performance similar to that of the exhaustive search method. However, when the number of users is large, the complexity of this technique becomes significantly higher. To deal with imperfect SIC, Ref. [27] applies DNN to allocate power in the downlink of the NOMA system. Although the DNN based power allocation scheme could achieve near-optimal performance in terms of energy efficiency at a much lower complexity, only a very small number of users is considered in their work. In another attempt, [PERSON] et al. proposed a convolutional neural network-based SIC for the downlink of the NOMA system to alleviate the sum-rate loss caused by imperfect SIC [28]. However, the maximum number of users considered in [28] is limited to four. In [29], a deep learning assisted receiver was developed for the uplink of the Faster than Nyquist (FTN) based NOMA system. However, the robustness of the technique with high-order modulations has not been investigated in [29]. To enhance the sum-rate of the NOMA system under the condition of imperfect SIC, a resource management framework based on DNN was designed in [30]. However, the work in [30] does not consider the scenarios in which the number of users dynamically changes in real-time. The prior work has laid down strong evidence that the DNN has the highest potential to achieve near-optimal performance among all the machine learning models. It is believed that DNN can explore deeper to capture the entire nonlinear transformations of clustering information that result in the precise prediction of UC to obtain a near-optimal throughput performance. In this paper, we propose a novel DNN-UC scheme by adapting the DNN architecture into the NOMA environment with the consideration of asymmetrical fading in a NOMA channel.