## 4 Simulation Results
Moreover, the proposed DNN-RSMA-based solution achieved a similar result to the near-optimal scheme of the PSO-SDR RSMA while significantly reducing the computational complexity. In particular, the complexity of the PSO-SDR method is denoted as \(\mathcal{O}(S\cdot T_{\text{max}}\cdot\mathcal{O}_{SDR})\), and the complexity of the DNN-based method is represented as \(\mathcal{O}(\mathcal{O}_{DNN}+\mathcal{O}_{SDR})\), where \(\mathcal{O}_{SDR}\) is the computational complexity of solving problem (7) using the SDR method, and \(\mathcal{O}_{DNN}\) represents the computational complexity of predicting the common rate variables. Consequently, the proposed DNN-based method was approximately \(5\times T_{\text{max}}\) times faster than the PSO-SDR near-optimal scheme. In addition, we observed that the RNN model achieved performance comparable to that of the proposed DNN. This similarity arises because when the input is a single vector, the RNN exhibits a layer-wise architecture similar to that of a basic neural network. Furthermore, the proposed DNN demonstrated lower computational time compared to the RNN. Specifically, the RNN required a computational time of 191.232 s for training and 0.002 s for testing, whereas the proposed DNN required a computational time of 40.295 s for training and 0.001 s for testing. These simulations were conducted on a computer equipped with an Intel Core i7-6700 CPU and 16 GB of RAM. Therefore, the proposed DNN emerges as the most suitable neural network architecture for solving the proposed optimization problem. Figure 6: Convergence behavior of the proposed DNN module. RSMA outperformed SDMA because of its capability to decode a portion of the interference through the SIC process on the common message (Figure 7), whereas SDMA treats all interference as noise. In the NOMA scheme, the difference arises from using the SIC process. In RSMA, the SIC process aims to cancel the interference from the common message. In contrast, in the NOMA scheme, the SIC process is employed to eliminate interference from the messages of users with weaker channel strengths. On the other hand, achieving excellent performance under the NOMA scheme in multi-antenna systems necessitates that users whose messages will be decoded by SIC have weaker channel strengths and sufficiently aligned channels. This alignment requirement is uncommon in real-world deployments and the channel model considered in Equation (13). Furthermore, as the rate requirement of the PS users increases, the transmission power needed for the beamforming vectors also increases, leading to performance degradation in the NOMA scheme after reaching \(\chi=4\) bits/s/Hz. The variation of the sum transmission power of the PB and BS with respect to the EH requirements, \(\varepsilon\), is presented in Figure 8, considering a minimum rate requirement of \(\chi=4\) bits/s/Hz. Similar to Figure 7, a significant reduction of approximately 3 dBm was observed in the total transmission power due to the deployment of the PB. Furthermore, the benefit of applying RSMA compared to conventional methods, such as NOMA and SDMA, in considerably reducing the transmission power was demonstrated. This is because RSMA employed the SIC procedure, which improved the data rate at the ID module of PS users while simultaneously reducing the PS factor to enhance harvested energy, thus reducing the transmission power. Furthermore, the proposed DNN-based approach performed similarly to the PSO-based method while significantly reducing computational complexity. Figure 8: Total transmission power of the PB and BS versus the required EH of PS and EH users. Figure 7: Total transmission power of the PB and BS versus the required data rate targets of PS users. Next, we analyzed the generalization performance of the proposed DNN-based method across considerably different scenarios from those used in the training phase.