Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.
Energy Rate Maximization with Sum-Rate Constraint for SWIPT in Multiple-Access Channels
