## 4 Simulation Results and Analysis  
### Simulation Settings  
In this section, simulations are extensively carried out to evaluate the performance of the proposed DNN-UC approach for different NOMA environments. Specifically, the  
Figure 2: Architecture of the proposed DNN-UC.  
MATLAB tool is employed to model the channels and networks in a NOMA-based 5G system. The channel gains between the BS and all the NOMA users are modeled by considering the shadowing effect with the attenuation factor \(S_{n}\) which is independently and identically distributed (i.i.d) with standard deviation, \(\sigma_{s}^{2}\). Besides, to model the urban non-line-of-sight propagation environment in the 5G network, the propagation path loss of \(v\) is used where path loss experienced by a user \(n\) is denoted as \(PL_{n}=d_{n}^{-v}\). The frequency-selective Rayleigh fading is adopted to model the multipath propagation environment where the correlations between the adjacent subcarriers are taken into account. The total bandwidth is partitioned in such a way that each subcarrier possesses a bandwidth that is much less than the coherence bandwidth of the channel so that each subcarrier experiences flat fading. In this work, we assume that the proposed 5G NOMA-based network is always geographically static in the sense that the time scale of the algorithm convergence is much shorter than the channel coherence time. In other words, the channel gains are always invariant in one implementation of the algorithm.  
The UC dataset adopted in this paper is generated by using the B-FS-UC scheme as described in Section 3.1. A total of 110,000 samples is collected and the maximum number of users considered is 50. Each sample has 150 attributes, which consists of 50 channel gain values, 50 transmit power values, and 50 cluster information values.  
The DNN architecture for the NOMA UC optimization is constructed by modifying the MATLAB deep learning toolboxes, which are then used to perform the training, validation, and testing for the proposed DNN-UC scheme. To optimize the hyper-parameters for the proposed DNN model and the selection of the best activation function, the DNN-UC is simulated for various scenarios (with different learning rates, different lengths of training data samples, different numbers of hidden layers, different numbers of epochs, and different batch sizes, etc.) to observe its performance in terms of MSE and throughput. With the optimized parameters, the performance of the DNN-UC is benchmarked with the OMA, DUC, ANN-UC, and B-FS-UC in terms of the throughput achievement for different network sizes. The simulation parameters for the proposed DNN-UC scheme and the downlink NOMA system are summarized in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of hidden layers & 4 \\ Number of data samples & 110,000 \\ Length of training data & 50,000, 60,000, 70,000, 80,000 \\ Length of validation data & 15,000 \\ Length of testing data & 15,000 \\ Learning rate & 0.1, 0.02, 0.01, 0.002, 0.001 \\ Number of input layer nodes & 48 \\ Number of hidden layer nodes & 40 \\ Number of output layer nodes & 24 \\ Batch sizes & 10, 20, 50, 100 \\ Activation functions & ReLu, Sigmoid, Sine, Tanh \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for the DNN-UC scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cell radius & 100 m \\ Bandwidth of the resource, \(B\) & 180 kHz \\ Downlink transmission power for cluster, \(p_{c}\) & 46 dBm \\ Detection threshold at SIC receiver, \(p_{min}\) & 10 dBm \\ Noise power & \(-\)118 dBm \\ Number of NOMA users & 2-24 \\ Number of subcarriers & 256 \\ Path loss & \(v\sim\mathcal{U}(3,\,5)\) \\ Shadowing effect & \(10\log_{10}S_{n}\sim\mathcal{N}(0,\sigma_{s}^{2})\), \(n\leq N\), \(\sigma_{s}^{2}\sim\mathcal{U}(4,\,10)\). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters for the downlink NOMA system.