Joint topology construction and power adjustment for UAV networks: A deep reinforcement learning based approach

W Xu, H Lei, J Shang - China Communications, 2021 - ieeexplore.ieee.org
In this paper, we investigate a backhaul framework jointly considering topology construction
and power adjustment for self-organizing UAV networks. To enhance the backhaul rate with …
被引用次数：13 相关文章

Decentralized trajectory and power control based on multi-agent deep reinforcement learning in UAV networks

B Chen, D Liu, L Hanzo - ICC 2022-IEEE International …, 2022 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) are capable of enhancing the coverage of existing
cellular networks by acting as aerial base stations (ABSs). Due to the limited on-board …
被引用次数：9 相关文章

Adaptive deployment of UAV-aided networks based on hybrid deep reinforcement learning

X Ma, S Hu, D Zhou, Y Zhou… - 2020 IEEE 92nd Vehicular …, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless
connections for ground users. Due to their constraints on both mobility and energy …
被引用次数：6 相关文章

Cellular-connected UAV trajectory design with connectivity constraint: A deep reinforcement learning approach

Y Gao, L Xiao, F Wu, D Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Cellular-connected unmanned aerial vehicle (UAV) communication has attracted
increasingly attention recently. We consider a cellular-connected UAV carried with limited on …
被引用次数：22 相关文章

Trajectory design and bandwidth assignment for UAVs-enabled communication network with multi-agent deep reinforcement learning

W Wang, Y Lin - 2021 IEEE 94th Vehicular Technology …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV) is considered as a promising technique to enhance future
wireless mobile communication. In this paper, the UAVs serve as aerial base stations …
被引用次数：6 相关文章

A time-efficient and attention-aware deployment strategy for UAV networks driven by deep reinforcement learning

J Wu, X Cheng, X Ma, W Li… - 2021 IEEE 94th Vehicular …, 2021 - ieeexplore.ieee.org
Collaborative unmanned aerial vehicle (UAV) networking has the characteristics of flexibility,
efficiency, ubiquity, etc., which can enhance wireless network coverage and improve the …
被引用次数：4 相关文章

User association and power allocation for UAV-assisted networks: A distributed reinforcement learning approach

X Guan, Y Huang, C Dong, Q Wu - China Communications, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be employed as aerial base stations (BSs) due to
their high mobility and flexible deployment. This paper focuses on a UAV-assisted wireless …
被引用次数：24 相关文章 所有 3 个版本

Deep reinforcement learning based resource allocation and trajectory planning in integrated sensing and communications UAV network

Y Qin, Z Zhang, X Li, W Huangfu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate
with on-ground target users. To optimize the communication and sensing performance, we …
被引用次数：24 相关文章 所有 2 个版本

Service-oriented topology reconfiguration of UAV networks with deep reinforcement learning

Z Chen, N Cheng, Z Yin, J He… - 2022 14th International …, 2022 - ieeexplore.ieee.org
The high mobility of UAVs makes it flexible to provide on-demand service function chains
(SFCs) for users in a large geographic area where the terrestrial network is usually not …
被引用次数：5 相关文章
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HAPS-UAV-enabled heterogeneous networks: A deep reinforcement learning approach

AH Arani, P Hu, Y Zhu - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude
platform station (HAPS) and low-altitude platform station (LAPS) has become essential …
被引用次数：10 相关文章 所有 3 个版本