## 4 Simulation Results and Analysis
### Simulation Results and Discussions
If two and three hidden layers are adopted in the DNN-UC, the throughput degrades by 6% and 5%, respectively, comparing to that achieved by the DNN-UC with four hidden layers. When the numbers of users and subcarriers are sufficiently large, the complexity of cluster formation and the power allocation becomes extremely high, increasing the dimensions of the nonconvexity of the UC dataset. As a result, the DNN-UC with 2 and 3 hidden layers is unable to cope with the large dimensions, which leads to inadequate approximation. On the other hand, the DNN-UC with four hidden layers demonstrates that the depth of the network suffices to produce the best generalization for the UC optimization. Nevertheless, further increasing the number of hidden layers to five and beyond does not rake in any beneficial impact to the MSE and throughput as the DNN-UC model becomes overfitting. The number of hidden nodes is important as choosing insufficient hidden dimensionality may result in inadequate signal detection in a complicated dataset while overwhelming hidden nodes may lead to overfitting. In this work, since the dataset is complex, the number of hidden nodes should be prudently chosen to produce the best performance. In Figure 8, different numbers of hidden nodes are tested on the 2-, 3-, 4-hidden-layer DNN-UC to observe their achievable throughput. In these three implementations, all the 2-, 3-, 4-hidden-layer DNN-UC demonstrates the same performance trend where the highest throughput can be obtained when the number of hidden nodes per layer is 40. Figure 6: Throughput performance achieved by the DNN-UC with respect to different lengths of training data samples for NOMA networks with different numbers of users. Figure 8: Throughput performance at different numbers of hidden layers for different numbers of hidden layer nodes. Figure 7: The impact of the number of hidden layers on the MSE and throughput performances achieved by the DNN-UC for different numbers of epochs. (Solid lines represent the MSE while dotted lines represent the throughput). To show the effectiveness of the proposed UC scheme, the DNN-UC is benchmarked with the BF-S-UC, ANN-UC, DUC, and OMA in terms of their throughput performance for different numbers of users. In this simulation, the DNN-UC uses all the best hyper-parameters, as suggested in the aforementioned investigations. In Figure 9, it is seen that the throughputs of the DUC, ANN-UC, and DNN-UC are upper-bounded by the BF-S-UC and lower-bounded by OMA. The suggestion of replacing OMA with NOMA in the upcoming 5G is solidified again by showing that the DNN-UC can improve the throughput capacity by four times, while other NOMA UC schemes also outperform the OMA. Besides, it is also noticed that the DNN-UC can achieve a near-optimal performance, approximately 97% of the throughput attained by the BF-S-UC for different network sizes. In comparison to the DUC, without fixing the number of users per cluster, the DNN-UC can gain more than 30% of throughput improvement, thanks to the full exploitation of channel diversity and heterogeneity among the users. Therefore, the more users that present in the network, the better performance can be acquired by the DNN-UC than the DUC as the diversity increases accordingly. The sub-optimality of the ANN-UC is partially solved by the DNN-UC, which includes more hidden layers in the architecture. The addition of more layers provides further room for hyper-parameter optimization, which allows the DNN-UC to build a better intuition to characterize the nonlinearity of cluster formation, channel gains, and power allocation. By and large, the DNN-UC can outperform the ANN-UC by 8-10% for different numbers of users.