## 4 Simulation Results and Analysis
### Simulation Results and Discussions  
First, the DNN-UC scheme is simulated with different activation functions to demonstrate their throughput achievements for different numbers of users in the NOMA system. The selection of the activation functions is one of the most important hyper-parameter optimizations in the DNN model, hence, 4 well-established functions, namely ReLu, Sigmoid, Sine, and Tanh are incorporated into the DNN-UC to observe their efficiency in UC. In Figure 3, it is seen that ReLu consistently outperforms other Sigmoid, Sine, and Tanh by 10%, 16%, and 18% in terms of throughput for \(K=24\). In general, the main issue faced by the ReLu function is the dead neuron resulting from the zero mappings for negative values. However, this is not an issue in the DNN-UC where all the inputs (channel gains and powers for all users) are non-zero where the computation that results in negative values is less likely to occur. Sigmoid, Sine, and Tanh functions exhibit a nonmonotonic activation behavior, which does not precisely characterize the cluster formation whose size is growing monotonically with the diversity of the channel gains and power allocation. Furthermore, it is well known that the Sigmoid and Tanh functions are vulnerable to the vanishing gradient issue when more hidden layers are employed, and the network may be unable to propagate useful gradient information for weights and biases adaptation during back-propagation. In consequence, the DNN-UC which is equipped with the Sigmoid or Tanh function may potentially suffer from poor convergence rates and local optima problems. Conversely, as the partial derivative of the ReLu function is always one for any positive inputs, the vanishing gradient issue could be effectively addressed for arbitrary network depth [31]. Therefore, to ensure the precise mapping and stability of the DNN during the training phase, ReLu is recommended for the proposed DNN-UC. For the following simulation, the ReLu function will be automatically embedded onto the DNN-UC for other hyper-parameter optimizations. It is also noteworthy that for each network scenario with different numbers of users, the users tend to form multiple clusters to enjoy higher throughput performance. For instance, in the scenario with 50 users, 13 clusters are formed, each sharing an exclusive set of subcarriers. With the implementation of NOMA and SIC, interference among the users within a cluster is minimized through efficient power allocation, and this leads to higher throughput achievement compared to its predecessor, i.e., the OMA scheme. Figure 3: The impact of different activation functions (ReLu, Sigmoid, Sine, and Tanh) on the achievable throughput by the DNN-UC for NOMA networks with different numbers of users. In Figure 4, the effects of batch size and the number of epochs on the training dynamics of the DNN-UC are investigated in the context of MSE at a learning rate of 0.001. Apparently, the MSE naturally manifests a declining tendency with the increasing number of epochs for all the different batch sizes. From the observation, further increasing the number of epochs certainly improves the MSE achievement, but this leads to a longer training duration. Furthermore, choosing a large number of epochs may also result in model overfitting, which reduces the generalization gap. Among all batch sizes, the batch size of 50 provides the fastest decaying rate, ultimately achieving an MSE of 0.2. The use of batch sizes of 10 and 20 does not produce satisfactory MSE scores, which are 0.4 and 0.3, respectively for 50 epochs. The UC problem defined in this work is a nonconvex NP-complete optimization problem. Small batch sizes may cause the output of the DNN-UC to bounce around the global optima depending on the ratio of the batch size to the dataset size. On the contrary, having a big batch size as large as 100 does not guarantee a better MSE performance as well because the UC problem which is also combinatorial in nature is difficult to be generalized well.