Multiple Green Base Stations Wireless Charging Schemes Based On Phase Adjusting

On account of the development of Internet of Things(Io T),Io T devices(Io TDs)and their novel applications are enriching people’s daily lives with convenience and efficiency.As the increasing number of Io TDs,traditional energy supply methods such as cables and batteries’ replacements will bring more extra manpower,money,and time costs.The generation of electricity may also aggravate air pollution and threaten public health.Recently,some remarkable researches show that green energy far-field wireless charging is a promising way to power the Io TDs in large-scale network remotely.However,low transmission power is the bottleneck that restricts the wide development of wireless farfield charging technology.Thus,this work focuses on improving the end-to-end transmission power of far-field wireless charging.In this work,we suggest to utilize multiple green base stations jointly to charge Io TDs and maximize the charging efficiency.We firstly propose the green energy farfield wireless charging network and GBS-Io TDs(Green Base Station-Io T devices)association.Considering the nonlinearity of multi-channel electromagnetic waves’ superposition and conversion circuit of Io TDs,we propose a nonlinear two-dimensional vector model and wireless energy conversion model.Afterward,we adjust the transmission phase of base stations to control the non-linear superposition of multiple electromagnetic waves according to the vector model.Due to the different moving statements of Io TDs,the charging scenarios can be divided into two categories,e.g.,stationary and dynamic scenario.For the stationary charging scenario,based on the nonlinear vector model and wireless energy conversion model,we propose a Two-step g REen energy wirel Ess charging(TREE)algorithm to efficiently power the static Io TDs.This algorithm optimizes the charging sequence in two steps.Io TDs with shorter charging time are scheduled to be charged firstly during the charging round.Moreover,those idle green base stations are scheduled to jointly charge Io TDs and enhance Io TDs’ received power.At the same time,we validate the performance of our proposed TREE by comparison with four algorithms.For the dynamic charging scenario,a dynamic moving model is proposed to simulate the moving trajectories of mobile Io TDs.Then,based on moving trajectories,the Greedy dyn Amic jo Int chargi Ng(GAIN)algorithm is put forward to schedule multiple green base stations efficiently power the Io TDs.This algorithm divides the charging round into multiple time slots,and dynamically determines the charging sequence at the beginning of each time slot.Similarly,the Io TDs with shorter charging time will be scheduled to get remote power supplement,and idle green base stations can help to jointly wirlessly charge Io TDs.At the same time,we validate the performance of our proposed GAIN by comparison with three algorithms.