## 5 Conclusions  
In this work, we investigated the transmit power optimization problem in SISO interference channels with SWIPT systems where a PS scheme was used at each receiver. The transmit power optimization problems subject to the required SINR and required harvested energy were investigated by optimizing transmit power and the PS ratio. After that, we exploited the ability of deep learning to improve the computing time in comparison with the traditional optimization algorithm. The various approaches based on deep learning were proposed such as FFNN, LRN, NARX, and the LSTM network. The performance of the proposed approaches was evaluated and compared to that of the traditional optimization algorithm. From experimental results, LSTM network provided the best balance in the tradeoff between solution quality (i.e., MSE) and solution efficiency (i.e., computation time) in the deep learning-based approaches. Overall, the experimental results showed that deep learning models can forecast output of the optimization problem effectively without prior knowledge about the system's state. Most of all, deep learning-based approach provided low computation time, compared to the traditional optimization algorithm, which is very useful for real-time resource allocation processing.  
From the simulation results, the FFNN is relatively simple to implement, so it has the lowest computation time. However, the FFNN provides inaccurate estimation of both transmit power and PS ratio. Due to the lack of control over the learning process, the FFNN further may lead to overfitting or underfitting to the training set, which makes predictions for new data be inaccurate. Traditional RNNs such as LRN and NARX have a \"memory\" which captures information about what has been calculated. In some cases of simulation results, traditional RNNs may improve network performance compared to the FFNN but in return longer computation time is required. However, traditional RNNs are influenced by the gradient vanishing and exploding problems, which makes the training of traditional RNN difficult, in two ways: (1) the RNN cannot process very long sequences if Hyperbolic tangent function is used as its activation function, (2) the RNN is very unstable if Rectified Linear Unit (ReLU) is used as its activation function. Besides, it cannot be stacked into very deep models. This is mostly due to the saturated activation function used in RNN models which makes the gradient decay over layers. LSTM network resolves these issues via Cell state (memory for LSTM) and Gates. In this paper, simulation results also show that the LSTM network can provide the best balance in the tradeoff between solution quality (i.e., MSE) and solution efficiency (i.e., computation time) compared to the other deep learning-based approaches.  
Conceptualization, H.T.T., P.-V.T. and I.K.; Formal analysis, H.T.T. and I.K.; Methodology, H.T.T.; Supervision, I.K.; Writing--original draft, P.-V.T.; Writing--review & editing, P.-V.T. and I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the National Research Foundation of Korea (NRF) grant through the Korean Government (MSIT) under Grant NRF-2018R1A2B6001714.  
The authors declare no conflict of interest.