东南大学学报（英文版）2020，Vol.36Issue(4)：393-398,6.DOI:10.3969/j.issn.1003-7985.2020.04.004
衰落信道下基于Q学习的能量调度方案
Q-learning-based energy transmission scheduling over a fading channel
摘要
Abstract
To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is considered.According to the channel state information(CSI)and the battery state,the charging duration of the battery is determined to jointly minimize the energy consumption of ES,the battery's deficit charges and overcharges during energy transmission.Then,the joint optimization problem is formulated using the weighted sum method.Using the ideas from the Q-learning algorithm,a Q-learning-based energy scheduling algorithm is proposed to solve this problem.Then,the Q-learning-based energy scheduling algorithm is compared with a constant strategy and an on-demand dynamic strategy in energy consumption,the battery's deficit charges and the battery's overcharges.The simulation results show that the proposed Q-learning-based energy scheduling algorithm can effectively improve the system stability in terms of the battery's deficit charges and overcharges.关键词
能量收集/信道状态信息/Q学习/传输调度Key words
energy harvesting/channel state information/Q-learning/transmission scheduling分类
交通工程引用本文复制引用
王志伟,王俊波,杨凡,林敏..衰落信道下基于Q学习的能量调度方案[J].东南大学学报（英文版）,2020,36(4):393-398,6.基金项目
Foundation item:The National Natural Science Foundation of China(No.51608115). （No.51608115）
