Masood Zaki, Choi Yonghoon
Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea.
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea.
Sensors (Basel). 2021 Nov 25;21(23):7857. doi: 10.3390/s21237857.
This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements.
本文提出了一种用于无线供电智能电网通信网络的物联网(IoT)智能电表,该电表具备节能的同时无线信息与功率传输(SWIPT)功能。带有能量收集(EH)的SWIPT技术是延长超低功耗设备电池寿命的一种有吸引力的解决方案。实现能效(EE)最大化的动机是提高能源的有效利用,并延长智能电表中嵌入式物联网设备的电池寿命。在系统模型中,智能电表配备了一个物联网设备,该设备在功率分配(PS)模式下实现SWIPT技术。本文旨在实现EE最大化,并考虑了下行链路中带有物联网功能的PS - SWIPT系统的智能电表的正交频分复用分布式天线系统(OFDM - DAS)。EE最大化是一个非线性非凸优化问题。我们通过拉格朗日方法和比例公平性,针对非凸EE最大化问题提出了一种最优功率分配算法,以在智能电表之间进行最优功率分配。所提出的算法显示出明显优势,即在有能量约束的EE最大化中考虑了总功耗。此外,还研究了EE与频谱效率(SE)之间的权衡。我们算法的结果表明,EE随着EH要求的提高而提高。