The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single ...The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.展开更多
为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决...为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决斗网络(dueling network,DN)结构以克服动态环境的部分可观测问题,联合优化了UAV-BS的位置和下行链路功率分配,在更符合实际的空地概率信道模型中检验了Dueling-DQN算法的性能。结果表明,相较于对比算法,所提出的Dueling-DQN算法可以提供更高的数据速率和服务公平性,且随着地面用户数量的增大,算法的优势更加明显。Dueling-DQN算法可有效解决复杂非凸性问题,为UAV-BS的资源分配问题提供理论参考。展开更多
无人机(unmanned aerial vehicle,UAV)作为移动基站(movable base stations,MBS),可有效提升蜂窝网络的通信性能。设计稳定的MBS与地面用户间的下行链路是该UAV-MBS网络的关键。为此,提出移动感知的混合射频与自由空间光(free space opt...无人机(unmanned aerial vehicle,UAV)作为移动基站(movable base stations,MBS),可有效提升蜂窝网络的通信性能。设计稳定的MBS与地面用户间的下行链路是该UAV-MBS网络的关键。为此,提出移动感知的混合射频与自由空间光(free space optical,FSO)下行链路方案。该方案依据MBS的移动状态对RF链路和FSO链路进行切换。该方案分别推导了RF链路、FSO链路和混合RF/FSO链路的误码率,并通过仿真分析上述3种链路的误码率性能。仿真结果表明,混合RF/FSO链路的误码率性能优于RF链路和FSO链路。展开更多
为保证用户服务质量(Quality of Service,QoS),针对用户分布不均匀状态,解决无人机基站以最小发射功率覆盖相同数量用户问题,提出一种基于最小包围圆和K-means算法的无人机优化部署方法。利用贪心算法思想将部署问题解耦为垂直和水平放...为保证用户服务质量(Quality of Service,QoS),针对用户分布不均匀状态,解决无人机基站以最小发射功率覆盖相同数量用户问题,提出一种基于最小包围圆和K-means算法的无人机优化部署方法。利用贪心算法思想将部署问题解耦为垂直和水平放置问题,分别通过求解最优性确定无人机基站三维部署位置。利用聚类分析方法构建用户簇,采用最小包围圆思想对无人机基站水平位置进行优化;以最小化通信能耗为目标,建立无人机基站通信模型确定无人机最优飞行高度;联合两种优化方案确定三维部署位置。针对用户不同分布状态对无人机基站发射功率的仿真实验,验证了部署方法的有效性。展开更多
基金supported in parts by the National Natural Science Foundation of China for Distinguished Young Scholar under Grant 61425012the National Science and Technology Major Projects for the New Generation of Broadband Wireless Communication Network under Grant 2017ZX03001014
文摘The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.
文摘为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决斗网络(dueling network,DN)结构以克服动态环境的部分可观测问题,联合优化了UAV-BS的位置和下行链路功率分配,在更符合实际的空地概率信道模型中检验了Dueling-DQN算法的性能。结果表明,相较于对比算法,所提出的Dueling-DQN算法可以提供更高的数据速率和服务公平性,且随着地面用户数量的增大,算法的优势更加明显。Dueling-DQN算法可有效解决复杂非凸性问题,为UAV-BS的资源分配问题提供理论参考。
文摘无人机(unmanned aerial vehicle,UAV)作为移动基站(movable base stations,MBS),可有效提升蜂窝网络的通信性能。设计稳定的MBS与地面用户间的下行链路是该UAV-MBS网络的关键。为此,提出移动感知的混合射频与自由空间光(free space optical,FSO)下行链路方案。该方案依据MBS的移动状态对RF链路和FSO链路进行切换。该方案分别推导了RF链路、FSO链路和混合RF/FSO链路的误码率,并通过仿真分析上述3种链路的误码率性能。仿真结果表明,混合RF/FSO链路的误码率性能优于RF链路和FSO链路。
文摘为保证用户服务质量(Quality of Service,QoS),针对用户分布不均匀状态,解决无人机基站以最小发射功率覆盖相同数量用户问题,提出一种基于最小包围圆和K-means算法的无人机优化部署方法。利用贪心算法思想将部署问题解耦为垂直和水平放置问题,分别通过求解最优性确定无人机基站三维部署位置。利用聚类分析方法构建用户簇,采用最小包围圆思想对无人机基站水平位置进行优化;以最小化通信能耗为目标,建立无人机基站通信模型确定无人机最优飞行高度;联合两种优化方案确定三维部署位置。针对用户不同分布状态对无人机基站发射功率的仿真实验,验证了部署方法的有效性。
