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 consi...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.展开更多
基金The National Natural Science Foundation of China(No.51608115).
文摘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.
