[PDF] ieee.org

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

QoE-driven adaptive deployment strategy of multi-UAV networks based on hybrid deep reinforcement learning

Y Zhou, X Ma, S Hu, D Zhou… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled
wireless connections for ground users. Due to their constraints on both mobility and energy …
被引用次数：23 相关文章

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：20 相关文章 所有 2 个版本

Trajectory design and generalization for UAV enabled networks: A deep reinforcement learning approach

X Li, Q Wang, J Liu, W Zhang - 2020 IEEE Wireless …, 2020 - ieeexplore.ieee.org
In this paper, an unmanned aerial vehicle (UAV) flies as a base station (BS) to provide
wireless communication service. We propose two algorithms for designing the trajectory of …
被引用次数：21 相关文章 所有 2 个版本
[PDF] ieee.org

Fairness-aware link optimization for space-terrestrial integrated networks: A reinforcement learning framework

AH Arani, P Hu, Y Zhu - IEEE Access, 2021 - ieeexplore.ieee.org
The integration of space and air components considering satellites and unmanned aerial
vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has …
被引用次数：27 相关文章 所有 3 个版本

UAV-assisted 5G/6G networks: Joint scheduling and resource allocation based on asynchronous reinforcement learning

H Yang, J Zhao, J Nie, N Kumar… - IEEE INFOCOM 2021 …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) for providing
wireless communications and coverage enhancement in fifth/sixth-generation (5G/6G) …
被引用次数：16 相关文章
[PDF] ieee.org

Trajectory design and resource allocation for multi-UAV networks: Deep reinforcement learning approaches

Z Chang, H Deng, L You, G Min, S Garg… - … on Network Science …, 2022 - ieeexplore.ieee.org
The future mobile communication system is expected to provide ubiquitous connectivity and
unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which …
被引用次数：22 相关文章 所有 4 个版本
[PDF] ieee.org

Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches

Y Bai, H Zhao, X Zhang, Z Chang… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …
被引用次数：27 相关文章 所有 5 个版本
[HTML] sciencedirect.com

[HTML][HTML] Communication-enabled deep reinforcement learning to optimise energy-efficiency in UAV-assisted networks

B Omoniwa, B Galkin, I Dusparic - Vehicular Communications, 2023 - Elsevier
Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless
connectivity to static and mobile ground users in situations of increased network demand or …
被引用次数：5 相关文章 所有 6 个版本
[PDF] arxiv.org

Bayesian optimization enhanced deep reinforcement learning for trajectory planning and network formation in multi-UAV networks

S Gong, M Wang, B Gu, W Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the
ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories …
被引用次数：19 相关文章 所有 6 个版本