通信学报2018，Vol.39Issue(4)：35-44,10.DOI:10.11959/j.issn.1000-436x.2018058
无线网络中基于深度Q学习的传输调度方案
Transmission scheduling scheme based on deep Q learning in wireless network
摘要
Abstract
To cope with the problem of data transmission in wireless networks, a deep Q learning based transmission scheduling scheme was proposed. The Markov decision process system model was formulated to describe the state transi-tion of the system. The Q learning algorithm was adopted to learn and explore the system states transition information in the case of unknown system states transition probability to obtain the approximate optimal strategy of the schedule node. In addition, when the system state scale was big, the deep learning method was employed to map the relation between state and behavior to solve the problem of the large amount of computation and storage space in Q learning process. The simulation results show that the proposed scheme can approach the optimal strategy based on strategy iteration in terms of power consumption, throughput, packets loss rate. And the proposed scheme has a lower complexity, which can solve the problem of the curse of dimensionality.关键词
无线网络传输/马尔可夫决策过程/Q学习/深度学习Key words
wireless network transmission/Markov decision process/Q learning/deep learning分类
信息技术与安全科学引用本文复制引用
朱江,王婷婷,宋永辉,刘亚利..无线网络中基于深度Q学习的传输调度方案[J].通信学报,2018,39(4):35-44,10.基金项目
国家自然科学基金资助项目(No.61102062,No.61271260,No.61301122) （No.61102062,No.61271260,No.61301122）
重庆市基础与前沿研究计划基金资助项目(No.cstc2015jcyjA40050)The National Natural Science Foundation of China(No.61102062,No.61271260,No.61301122),Chongqing Research Program of Basic Research and Frontier Technology(No.cstc2015jcyjA40050) （No.cstc2015jcyjA40050）
