## 4 Simulation Results
In particular, we kept the trained DNN module fixed and tested its generalization performance by varying the number of antennas, resulting in a modification of the components of the channel vectors, and by varying the distance from the BS, resulting in a significant alteration of the channel strengths. The channel vectors, along with the requirements for data rates and energy harvesting, served as inputs for the PCA and DNN modules in the proposed scheme to generate the common rate variables, as detailed in Section 3.1. Figure 9 presents the variation of the sum transmission power of the PB and BS with respect to the number of antennas equipped at the BS, considering a required minimum rate of \(\chi=4\) bits/s/Hz and a required minimum EH of \(\varepsilon=-25\) dBm. As the number of antennas at the BS increased, there was a reduction in the transmission power due to the increased degrees of freedom. In the case of the DNN-based method, the training dataset solely consisted of samples representing a scenario with a BS equipped with eight antennas, as detailed at the beginning of Section 4. In contrast, the testing data in Figure 9 contained samples encompassing scenarios with varying numbers of antennas at the BS. The utilization of the same trained DNN module with different numbers of antennas can be attributed to the PCA module. In particular, independent PCA modules for each scenario were trained based on the number of antennas while maintaining the same number of principal components, \(L\). Consequently, despite variations in the dimensionality of the channel vectors, the output of the PCA module remained consistent across all different numbers of antennas. Training a PCA module is a straightforward task because it only requires channel vector samples and does not necessitate labels or target values. The proposed DNN-based scheme demonstrated remarkable performance comparable to that of the PSO-based method, even when the number of antennas differed from the training scenarios (Figure 9). This indicates strong generalization capabilities and robustness to environmental changes. Furthermore, the RSMA-based methods consistently achieved lower total transmission power compared to the traditional NOMA and SDMA methods. Moreover, the deployment of the PB resulted in a significant decrease in the transmission power, even with an increasing number of antennas at the BS. This can be attributed to the ability of the PB to mitigate signal attenuation because of the distance from the transmitter, a critical factor in scenarios with EH requirements for users. Figure 10 shows the variation of the sum transmission power of the PB and BS with respect to the position of the PS users, considering a minimum rate requirement of \(\chi=4\) bits/s/Hz and a required EH of \(\varepsilon=-25\) dBm. In particular, the range of the position of the PS users on the x-axis, \(x_{PS}\), was varied. This position was randomly selected within the region of \(x_{PS}\in[d_{x}\) m, \((d_{x}+5)\) m], where \(d_{x}\) varied to analyze performance as the distance between the PS users and the BS increased. The transmission power increased  
Figure 9: Total transmission power of the PB and BS versus the number of antennas at the BS. as \(d_{x}\) increased because the average distance from the user to the BS also increased with higher values of \(d_{x}\). Additionally, the difference in the transmission power between scenarios with and without the PB diminished as the value of \(d_{x}\) increased, owing to the fixed location of the PB across all \(d_{x}\) values. As \(d_{x}\) increased, users were positioned farther from the PB, resulting in increased attenuation of the energy signal. Although the energy signal at the PS users was utilized for energy harvesting at the EH module, it was considered interference for the ID module. Consequently, as the received energy signal power from the PB diminished due to increased distance, it contributed less to the EH at the PS users.