Deep Learning-Based Approach to Fast Power Allocation in SISO SWIPT Systems with a Power-Splitting Scheme

Abstract

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.

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

“…To evaluate the performance of this approach, it may be tested for resource allocation solution prediction accuracy. Examples of the proposed supervised DL approaches for resource management include [107], [118], [119], and [120]. In [118], a supervised DL based approach was presented to predict an optimal transmit power for different channel coefficients in a wireless powered communication network (WPCN).…”

“…Disadvantages: The computation time of the proposed MLP model is low. In [119], a supervised DL based approach was presented to predict the optimal transmit power and PS ratios resource allocation that can minimize the sum-transmit-power of a SWIPT-based IoT system. They used a conventional optimization algorithm to solve the optimization problem of the paper.…”

“…They used a conventional optimization algorithm to solve the optimization problem of the paper. The power and PS ratios resource allocation solutions (i.e., output labels) computed by the optimization algorithm with their correspondence channel vectors (i.e., input labels) were learned by using four DL architectures like FFNN and RNN architectures like LSTM, Nonlinear AutoRegressive network with eXogeneous inputs (NARX) [119], and layer recurrent network (LRN). The performance of the models was evaluated in terms of prediction accuracy using MSE.…”