## 4 Numerical Results and Discussions
### The Simulation Setup and Neural Network Parameter Selection
Therefore, we present the computation time of the optimization scheme in minutes in order to facilitate observation. Overall, the computation time of the deep learning-based approaches is much lower when compared to the optimization algorithm-based approach. This is easily explained because the testing stage does not involve iterative optimization; it only needs some layers and simple operations like matrix-vector multiplication/addition and simple nonlinear transformations. Therefore, if the training stage can estimate the optimization algorithm well enough, the computation time for resource allocation in real-time can be reduced significantly. In addition, we also tested network performance by increasing the number of hidden layers in the selection process of neural network parameter in terms of MSE and computation time when networks used 20 neurons in the hidden layers, as shown in Figures 5 and 6. Figure 5 shows that the MSE value changed very slightly among different schemes when the number of hidden layers is increased from 2 to 6. Figure 6 shows the computation time of both training and testing stages where 10,000 samples are used for training in the FFNN, NARX, LRN and LSTM, and 1000 samples are used for testing. In the testing stage, we measured the computation time of the optimization scheme to get 1000 samples, for the purpose of computation time comparison between deep-learning approach and optimization approach. Figure 6a shows that the computation time of all deep-learning networks increases very slightly in the training stage as the number of hidden layers increases. Among deep-learning networks, LSTM has the largest computation time. Figure 6b also shows that the computation time of all deep-learning networks increases very slightly in the testing stage as the number of hidden layers increases. However, it is noteworthy that all deep-learning networks have very small amount of computation time, compared to that of optimization scheme. From the simulation results in Figures 3-6, we have chosen 20 neurons and two hidden layers for the next simulation results with which we can get the best balance in the tradeoff between MSE and computation time. The next simulation results provide more insight on the capability of the proposed deep learning-based approaches that can capture responses produced by the optimization algorithm in changing the required SINR and the required harvested energy. Figure 3: The MSE of deep learning-based approaches in the testing stage when the size of hidden layer is 20, 40, and 60 neurons, respectively. Figure 4: The computation time of training stage and testing stage when the size of hidden layer is 20, 40, and 60 neurons, respectively: (**a**) Computation time among FFNN, NARX, LRN and LSTM in the training stage. (**b**) Computation time among FFNN, NARX, LRN, and LSTM and optimization scheme in the testing stage. Figure 5: The MSE of deep learning-based approaches in the testing stage when the number of hidden layers is 2, 4, and 6, respectively. Figure 6: The computation time of training stage and testing stage when the number of hidden layers is 2, 4, and 6 layers respectively: (**a**) Computation time among FFNN, NARX, LRN and LSTM in the training stage. (**b**) Computation time among FFNN, NARX, LRN, and LSTM and optimization scheme in the testing stage.