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An Overview of Intelligent Wireless Communications Using Deep Reinforcement Learning

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Huang Yongming 1,2 *   Xu Chunmei 1,2   Zhang Cheng 1,2   Hua Meng 1,2   Zhang Zhengming 1,2  
文摘 Future wireless communication networks tend to be intelligentized to accomplish the missions that cannot be preprogrammed. In the new intelligent communication systems, optimizing the network performance has become a challenge due to the ever-increasing complexity of the network environment. New theories and technologies for intelligent wireless communications have obtained widespread attention, among which deep reinforcement learning (DRL) is an excellent machine learning technology. DRL has great potential in enhancing the intelligence of wireless communication systems while overcoming the above challenge. This paper presents a review on applications of DRL in intelligent wireless communications with focuses on millimeter wave (mmWave), intelligent caching and unmanned aerial vehicle (UAV) scenarios. We first introduce the concepts and basic principles of single/multi- agent DRL techniques. Then, we review the related works where DRL algorithms are used to address emerging issues in wireless communications. These issues include mmWave communication, intelligent caching, UAV aided communication, and handover/access control in HetNets. Finally, critical challenges and future research directions of applying DRL in intelligent wireless communications are outlined.
来源 Journal of Communications and Information Networks ,2019,4(2):15-29 【核心库】
DOI 10.23919/JCIN.2019.8917869
关键词 deep reinforcement learning ; multi-agent reinforcement learning ; intelligent wireless communications ; mmWave ; caching ; UAV
地址

1. School of Information Science and Engineering, Southeast University, Nanjing, 210096  

2. Purple Mountain Laboratories, Nanjing, 211111

语种 英文
文献类型 研究性论文
ISSN 2096-1081
学科 电子技术、通信技术
基金 the Research Project of Jiangsu Province ;  国家科技重大项目 ;  国家自然科学基金
文献收藏号 CSCD:6534046

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