## 4 Simulation Results
The testing dataset encompassed scenarios not encountered during the training and validation phases to assess the generalization performance of the model. These scenarios included different user distances and varying numbers of antennas at the BS. The training/validation dataset consisted of 14,000 samples, covering data rates ranging from \(\chi=1\) bits/s/Hz to \(\chi=10\) bits/s/Hz, EH requirements from \(\varepsilon=-16\) dBm to \(\varepsilon=-30\) dBm, eight antennas at the BS, and the previously mentioned range of distances. The specifics of the testing dataset for each scenario are outlined later in this paper. In addition, the exponential linear unit (ELU) was selected as the activation function for the hidden layers, and the Adam algorithm was used as the optimizer. The training data for the PCA module were composed of 6000 channel vectors. Regarding the PSO-SDR method, the simulation parameters considered for the PSO algorithm after fine-tuning the parameters were \(S=15\), \(w=0.7\), \(a_{c}=b_{c}=1.494\), and \(T_{max}=25\). Firstly, this paper presents an analysis of the convergence behavior of the DNN module while considering different hyperparameters. Figure 6 shows the convergence performance of the DNN module under varying learning rates (lr), numbers of hidden layers, and numbers of principal components (PCs) in the PCA module. In the legend in Figure 6, the last term represents the number of units \(\times\) the number of hidden layers, where all hidden layers have the same number of hidden units. Regarding the learning rate, a high learning rate leads to rapid convergence, but it can also result in overfitting, as observed in the case of lr = 0.005. On the other hand, low learning rate values require a significantly higher number of epochs to achieve convergence, and they may not guarantee the lowest error value, as observed in the case of lr = 0.0001. Regarding the number of PCs, it is important to note that the PCA module was applied to each channel vector, resulting in each channel vector being represented by a certain number of PCs. The lowest error was achieved when the number of PCs was 2 or 3. In particular, when PC = 2, it implies a lower number of required input nodes in the DNN module. For the remainder of the simulations, a learning rate of lr = 0.001 and PC = 2 were selected based on these observations. We utilized the recurrent neural network (RNN) model [38] in our simulation results for comparative analysis. RNN, a type of neural network, is specifically designed to analyze sequences of vectors, where values in successive vectors exhibit interrelationships. However, it is noteworthy that each sample in our dataset comprised a singular vector containing the user's rate requirements, EH requirements, and the reduced representation of the channel vectors post-PCA module. To incorporate the RNN into our simulations, we configured the number of vectors in the sequence to one and set the size of the hidden layer to 150. Figure 7 shows the variation of the sum transmission power of the PB and BS with respect to the data rate requirements, \(\chi\), considering a minimum EH requirement of \(\epsilon=-24\) dBm. The data rate requirement serves as a constraint in the proposed optimization problem (5) and must be satisfied by the BS for all users in the system. As the rate requirement increases, the BS must allocate more transmission power to meet the specified rate, thereby increasing the total transmission power. Although the PB does not transmit information signals, their energy-carrying signals aid in meeting the EH requirements but are considered interference at the ID module of PS users. In particular, we observed that deploying the PB enabled a reduction of up to 3 dBm in the total transmission power for all the multiple-access methods compared. Furthermore, the RSMA scheme achieved significantly lower transmission power with and without the PB compared to their respective SDMA- and NOMA-based counterparts.