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 perfor-mance has become a c...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 perfor-mance 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 com-munications with focuses on millimeter wave(mmWave),intelligent caching and unmanned aerial vehicle(UAV)scenarios.We first introduce the concepts and basic prin-ciples 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.展开更多
基金supported by the National Natural Science Foundation of China under Grant 61720106003the National Science and Technology Major Project of China under Grant 2018ZX03001002-003the Research Project of Jiangsu Province under Grant BE2018121。
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 perfor-mance 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 com-munications with focuses on millimeter wave(mmWave),intelligent caching and unmanned aerial vehicle(UAV)scenarios.We first introduce the concepts and basic prin-ciples 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.
