## 1 Introduction
Therefore, real-time operation of transmit power control still faces much difficulties in practical use. The problem becomes more serious when the number of more iteration is essential as the number of users increases [17]. For instance, the weighted minimum mean square error (WMMSE) algorithm needs complicated operations like the inversion of matrix [18; 19], singular value decomposition [20; 21] in each iteration, and semidefinite relaxation [22; 23]. Recently, using deep learning technology has been investigated in many domains, e.g., image processing, and it has proven superior performance compared to conventional schemes [24; 25; 26; 27]. In particular, deep learning has started to be dealt with wireless communication systems, e.g., communication signals classification [25], channel estimation and signal detection [27], indoor localization [28], sparse optimization [29], and the optimization of constellation mapping [30]. Besides, through deep neural networks (DNNs), deep learning technology can be used to efficiently solve sophisticated nonlinear problems through back-propagation algorithms [17] with low computation time [27] without the need to derive complex mathematical models. The authors in [17] approximated the transmit power based on WMMSE that was studied in [18] using a dense neural network, which can solve the main shortcoming of the WMMSE-based algorithm, for example, a long computation time caused by a huge number of iterations. Moreover, the validity of using a DNN in practical systems of wireless communication fields was confirmed in [31] using a testbed. Unlike conventional resource management schemes in which sophisticated optimization problems have to be dealt with an iterative manner, we propose a deep learning-based approach for wireless resources allocation (in particular, for transmit power management) that can be obtained with low computation time. The basic idea is to use a set of wireless resources that is solved by an optimization algorithm, and to attempt to learn its input and output by using deep learning technologies based on DNNs or recurrent neural networks (RNNs) [32]. If a network efficiently approximates to a wireless resource allocation optimization algorithm, then the input of the optimization algorithm is passed into the trained network to get the output with higher computational efficiency in comparison with the optimization algorithm. This is because its 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 efficiently approximate the optimization algorithm, the computation time for resource allocation in real time can be reduced significantly. Overall, our approach can be regarded as using a deep learning technology which based on DNNs and RNNs models for approximating an iterative optimization algorithm according to given network parameters. The main contributions of this paper are summarized as follows. * We investigate a SWIPT system with multiple single-antenna at transmitters and receivers with a PS structure. The information and energy are transmitted simultaneously from transmitters to receivers. The transmit power optimization problem subjects to required SINR and energy harvesting is investigated by optimizing transmit power and the PS ratio at the receiver. * We propose a deep learning-based approach to the transmit power optimization problem in SISO interference channels with SWIPT systems. The proposed approach opens up new research directions with a combination of the two fields of deep learning and wireless resources management (specifically, the combination of deep learning architectures and the transmit power optimization problem over interference channels).