## 4 Numerical Results and Discussions
### Network Performance in Changing of Required SINR and Required Harvesting Energy Values  
In this section, we provided the simulation results and discussions to verify the capability and efficiency of the proposed deep learning-based approaches in scenarios for changing the required SINR and required harvested energy. We employed two network performance metrics in the performance evaluation (transmit power and receiver PS ratio) with various network conditions, such as required SINR and required harvested energy. We also used two hidden layers, and 20 neurons for each hidden layer, as discussed in Section 4.1. Network condition parameters set for either changing the required SINR or the required energy harvesting. In case of changing the required SINR, we fixed the required harvested energy value at \(-20\) dBm and the required SINR is changed from 2 dB to 6 dB, as shown in Figures 7 and 8 for the sum of the transmit powers and the average PS ratios, respectively. In the other cases, i.e, changing the required energy harvesting, we fixed required SINR value at 2 dB, and changed the required harvested energy from \(-20\) dBm to \(-12\) dBm, as shown in Figures 9 and 10. In Figures 7 and 8, we show the sum of transmit powers and the average PS ratios, respectively, when changing the required SINR in the training and testing stages. Overall, the schemes mostly have an upward trend in both transmit power and PS ratio as the required SINR increases. This is because when the required SINR increases, the power of the transmitter must increase in order to guarantee the information services at the receivers. Moreover, the PS ratio increases to guarantee that the transmission is more effective so constraint Equation (4b) can be guaranteed. When the required SINR is small, transmitters only use a small amount of power for transmission. This makes the fluctuation of transmit power among samples insignificant, and thus, the training and testing stages perform effectively, especially the sum of transmit powers (from 2 dB to 5 dB for the required SINR) and the average PS ratios (from 2 dB to 4 dB for the required SINR), as shown in Figures 7 and 8, respectively. The result is that the deep learning-based approaches can capture a response similar to the response of the optimization algorithm in both training and testing stages. In other words, due to a relatively large fluctuation of transmit power that exists in the training data set at the high required SINR value (\(\gamma_{k}\) = 6 dB), the results of the training stage are less effective. Then, the testing stage will inevitably be less effective, as shown in Figure 7b (at \(\gamma_{k}\) = 6 dB) and Figure 8b (at \(\gamma_{k}\) = 5 dB and \(\gamma_{k}\) = 6 dB). Noteworthy is Figure 8 at \(\gamma_{k}\) = 6 dB, where the results of the training and testing stages are not good, which make the PS ratio no longer significantly desirable (\(\theta_{k}\
otin(0,1)\)) in the FFNN and LRN approaches when a relatively large fluctuation of transmit power exists in the considered data. Unfortunately, the training process of traditional RNNs is influenced by an issue in backpropagation  
Figure 10: Average power-splitting ratios according to the required energy harvesting when the required SINR is given by 2 dB (\(\gamma_{k}\) = 2 dB). Figure 9: Sum of transmit powers according to the required energy harvesting when the required SINR is given by 2 dB (\(\gamma_{k}\) = 2 dB). through time, called the exploding gradient (which can cause learning to diverge) or by the vanishing gradient, where learning either becomes very slow or stops working altogether (which prevents complete learning of the time series). One solution is to utilize LSTM networks. Therefore, in the comparison of simulation results in Figures 7 and 8, LSTM has superior performance compared to the other deep learning-based approaches (in particular, in the average PS ratio for both training and testing stages as shown in Figure 8).