## 4 Simulation Results  
Numerical simulations were conducted to assess the performance of the proposed DL-based solution in minimizing the sum transmission power of the BS and PB in the considered MISO SWIPT system with RSMA. The simulation parameters were set to \(K=3\) PS users, \(G=2\) EH users, \(M=8\) antennas at the BS, \(N=8\) antennas at the PB, \(\sigma_{k}^{2}=\gamma_{k}^{2}=-60\) dBm, \(\xi_{k}^{PS}=\xi_{g}^{EH}=1\), \(\chi_{k}=\chi\), and \(\varepsilon_{k}^{PS}=\xi_{g}^{EH}=\varepsilon\). The channel vector between the \(k\)th PS user and the BS is given by  
\[\mathbf{q}_{k,PS}=\sqrt{\beta d_{BS-PSK}^{-\
u}}\mathbf{\tilde{q}}_{k,PS},\, \forall k, \tag{19}\]  
where \(\
u=2.2\) defines the path-loss exponent; \(\beta=10^{-3}\); \(d_{BS-PSK}^{-\
u}\) denotes the distance between the BS and the \(k\)th PS user; and \(\mathbf{\tilde{q}}_{k,PS}\) is subject to independent Rician fading. \(\mathbf{q}_{g,EH},\mathbf{h}_{k,PS},\mathbf{h}_{g,EH}\) are channels established according to (13). The BS is positioned at coordinates (8 m, 20 m), whereas the PB is located at (13 m, 7 m). PS users are randomly distributed within a designated region defined by \(x_{PS}\in[13\) m, 18 m] and \(y_{PS}\in[13\) m, 25 m]. Similarly, EH users are randomly distributed within an area bounded by \(x_{EH}\in[17\) m, 22 m] and \(y_{EH}\in[2\) m, 12 m]. The effectiveness of the proposed DL-based method was evaluated by comparing it with a PSO-SDR method and conventional techniques, such as SDMA and NOMA. The proposed scheme is denoted as DNN-RSMA, and the comparative scheme based on PSO and SDR is denoted as PSO-RSMA. In SDMA [26], the \(k\)th PS user's message, \(W_{k}\), is encoded directly into the data stream, \(\mathbf{z}_{k}^{PS}\), without common rate variables, and interference originating from other users is considered noise. In addition, a comparative scenario was incorporated by assuming the absence of the power beacon (PB) deployment in the network. In NOMA [27], interference originating from other users is mitigated by employing multiple layers in the SIC process. The message intended for the \(k\)th PS user, \(W_{k}\), is encoded into the private stream, \(z_{k,N}^{PS}\). The transmitted signal at the BS is given by \(\mathbf{x}_{BS,NOMA}=\sum\limits_{k=1}^{K}\mathbf{p}_{k,N}z_{k,N}^{PS}\), where \(\mathbf{p}_{k,N}\in\mathbb{C}^{M\times 1}\) is the information beamforming vector for \(z_{k,N}^{PS}\). The signal transmitted at the PB is the same as in the proposed RSMA-based framework. On the user side, the decoding order for PS users is determined by their respective channel strengths, denoted as \(\|\mathbf{q}_{1,PS}\|\geq\ldots\geq\|\mathbf{q}_{K,PS}\|\). In particular, the \(k\)th PS user begins by decoding messages intended for the \(K\)th, \((K-1)\)th,..., \((k+1)\)th PS users. Subsequently, the \(k\)th PS user proceeds to decode its intended message while treating the interference from the remaining messages as noise. The achievable rate to decode the \(l\)th message at the \(k\)th PS user is given by  
\[\mathrm{R}_{k,NOMA}^{l}=\log_{2}\left(1+\frac{\left|\mathbf{q}_{k,PS}^{H} \mathbf{p}_{l,N}\right|^{2}}{\sum\limits_{k=1}^{I-1}\left|\mathbf{q}_{k,PS}^{ H}\mathbf{p}_{k^{\prime},N}\right|^{2}+\sum\limits_{g=1}^{G}\left|\mathbf{h}_{k, PS}^{H}\mathbf{e}_{g}\right|^{2}+\sigma_{k}^{2}+\frac{\gamma_{k}^{2}}{ \theta_{k}}}\right),\forall k,\forall l\geq k. \tag{20}\]  
Two datasets were considered in the simulation analysis: a training/validation dataset and a testing dataset. As described in Section 3, the PSO-SDR method was used to process all the datasets considered. A fivefold cross-validation method was used to divide the training/validation dataset into separate training and validation datasets. These datasets were used to determine the optimal hyperparameters for the DNN module, including the number of hidden layers, hidden nodes, learning rates, activation functions, and batch sizes. Subsequently, the testing dataset was used to evaluate the ability of the model to generalize.