Energy-Efficiency Optimization for IoT-Distributed Antenna Systems With SWIPT Over Composite Fading Channels

Abstract

Due to the requirement of low-power network devices for the proliferation of high data communication, enabling technologies for energy sustainable Internet of Things (IoT) are of great significance. In this article, we investigate the energy-efficiency (EE) optimization of IoT-distributed antenna (DA) system with simultaneous wireless information and power transfer (SWIPT) technique over fading channels, where the IoT device is equipped with power splitter to integrate the energy harvesting and information decoding processes via adjusting the transmit power of each DA port and power splitting (PS) ratio of IoT device. According to the analysis of EE, an optimization problem with the aim of maximizing the system EE is formulated under the constraints of maximum transmit power of each DA port as well as minimum harvested energy. Through analyzing the structure of the objective problem, it is found that the EE optimization problem, which combines transmit power allocation (PA) with PS problem, can be predigested to a PA problem. Then, the amount of valid DA ports and the corresponding PA are achieved by utilizing the Karush–Kuhn–Tucker conditions and Lambert function. Based on this, we propose an optimal resource allocation scheme without iteration to obtain the optimal PA and PS ratio, and the resulting closed-form expressions are provided. Considering that perfect channel state information (CSI) is hard to achieve, we also study the resource allocation scheme based on the imperfect CSI (i.e., statistical CSI with large-scale fading information), two suboptimal schemes are proposed. These two schemes have better robustness and lower complexity than the optimal scheme with perfect CSI because they only need partial channel information, but the performances are worse than the latter, as expected. Computer simulation indicates that proposed schemes are valid and can obtain superior EE performance with lower complexity.