## 5 Conclusions  
In this work, the DNN model has been proven to be feasible and efficient in solving the UC problem for NOMA 5G networks. With the datasets generated by the B-FS-UC, the proposed DNN-UC can be trained, validated, and tested. The inclusion of more hidden layers in the DNN model has facilitated the DNN-UC to better characterize the nonlinear transformation of diversity in channel gains and powers into cluster formation. After the cluster formation, an efficient power allocation scheme is implemented to ensure all users can achieve the required minimum throughput subject to the SIC constraint in each  
Figure 9: Comparison of throughput performance among the DNN-UC, B-FS-UC, ANN-UC, DUC, and OMA in NOMA networks with different numbers of users.  
cluster. Extensive simulations have been carried out to optimize the hyper-parameters of the training for the DNN-UC. From the investigation, it is concluded that the DNN-UC functions the best when the structure adopts four hidden layers, each with 40 nodes, and trained with the 33,600 data samples using a batch size of 50 at a learning rate of 0.001 for 50 epochs. With the best hyper-parameters, the proposed DNN-UC scheme is capable of attaining a near-optimal performance in terms of throughput, which is approximately 97% of throughput achieved by the B-FS-UC method. Furthermore, the robustness of the DNN-UC is also evaluated in various NOMA environments, which reveals that the proposed method is adaptive and robust against any NOMA environments without retraining the model. However, owing to the deep architecture of the proposed method which involves multiple levels of processing and nonlinear transformations, the learning process of the DNN-UC is slower and the computational complexity for the testing phase is higher compared to the ANN-UC. For future work, it is suggested to look into the complexity reduction issue for the DNN-UC, maybe adopting a deep learning pruning approach or using faster deep learning variants to solve the UC problem.  
Nevertheless, owing to the deep architecture of the proposed method which involves multiple levels of processing and nonlinear transformations, the learning process of the DNN-UC is slower and the computational complexity for the testing phase is higher compared to the ANN-UC. When the depth and the width of DNN-UC are increased to cater for more complex deployment scenarios, the aforementioned issues would be exacerbated and the DNN-UC would also be more prone to overfitting. As such, for future work, it is suggested to look into model and complexity reduction issues for the DNN-UC, maybe adopting a deep learning pruning approach to eliminate redundant weights, using faster deep learning variants or advanced weight adaptation strategies to enhance its learning speed and computational efficiency.  
Conceptualization, S.P.K.; methodology, S.P.K.; software, S.P.K.; supervision, C.K.T. and Y.H.N.; writing--original draft preparation, S.P.K.; writing--review and editing, C.K.T. and Y.H.N. All authors have read and agreed to the published version of the manuscript. This research was funded by Multimedia University under grant number MMUI/170084. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.