## 1 Introduction
A previous study [25] considered the coexistence of a multi-antenna PB with a multi-antenna BS, assuming one ID user and several EH users, where the authors addressed the maximization of the total harvested energy. On the other hand, the aforementioned works did not consider SWIPT users equipped with a PS architecture and did not incorporate the RSMA framework. Regarding state-of-the-art SWIPT systems assisted by a PB, the minimization of transmit power in a single-antenna system was investigated in [10]. Extensions to a multiuser MISO system incorporating SDMA and NOMA were introduced in [26; 27], respectively. [PERSON] et al. [28] considered a scenario in which a multi-antenna PB transmits RF energy to a single-antenna transmitter, serving two users with several relays. The transmitter employed the NOMA method and harvested energy from the PB to transmit the two messages. The relay applied the PS architecture to harvest energy and decode the message for the distant user, which was then forwarded. A part of this investigation was presented in a conference article [29], where the SWIPT system with RSMA was introduced with the aid of a PB, where the optimal scenario of decoding the whole interference from the PB at the user side was assumed. On the other hand, this paper considered the general case of treating the interference from the PB as noise. In addition, a low-complexity and efficient scheme was proposed based on deep learning (DL). DL is gaining popularity as a technique for resource optimization in wireless networks because of its ability to significantly reduce computational complexity compared to traditional optimization methods. A review of the most common schemes based on machine learning and reinforcement learning methods for network optimization problems was published in [30; 31]. A deep neural network (DNN) was introduced as an approximation method for solving the sum-rate maximization problem using the WMMSE algorithm in a single-antenna system [32]. The results of the simulations demonstrated the effectiveness of the DNN, which closely approximated the solution of the WMMSE algorithm while reducing the computational time. [PERSON] et al. [33] addressed three popular optimization prob lems in a multiuser MISO system with SDMA. Their proposed solution leveraged a neural network module to predict key features based on the channel vectors and a beamforming module to construct the beamforming vector from the predicted vital features. Building upon the previous system model, the sum-rate maximization problem was investigated in [34], and two types of schemes based on a DNN were proposed: one where the beamformers are directly generated by the DNN output and the other utilizing a beamformer recovery module after the DNN module. The simulation results demonstrated performance close to traditional optimization methods, with the model incorporating the beamformer recovery module yielding the best results. On the other hand, the aforementioned work did not consider SWIPT technology and the RSMA method. The solutions are problem-dependent and rely on expert knowledge. Furthermore, state-of-the-art methods consider the channel vectors as direct inputs to the deep learning module. This approach involves using hundreds of features as the number of antennas and users increases, increasing the complexity of the deep learning model. A previous work [35] considered a MISO SWIPT system with RSMA, where a deep learning-based solution was proposed. The scheme comprised three modules: an autoencoder for dimension reduction, a DNN to predict a set of target variables, and a precoding module to obtain the precoding vectors. On the other hand, the authors did not account for EH users or the deployment of a PB, which significantly increased the number of input features. Furthermore, the autoencoder module required problem-dependent adjustments of its hyperparameters, such as the number of hidden layers, hidden nodes, activation functions, and learning rate.