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 …
with on-ground target users. To optimize the communication and sensing performance, we …
Deep reinforcement learning for joint trajectory planning, transmission scheduling, and access control in UAV-assisted wireless sensor networks
Unmanned aerial vehicles (UAVs) can be used to relay sensing information and
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
Cellular-connected UAV trajectory design with connectivity constraint: A deep reinforcement learning approach
Cellular-connected unmanned aerial vehicle (UAV) communication has attracted
increasingly attention recently. We consider a cellular-connected UAV carried with limited on …
increasingly attention recently. We consider a cellular-connected UAV carried with limited on …
Resource allocation in UAV-assisted networks: A clustering-aided reinforcement learning approach
S Zhou, Y Cheng, X Lei, Q Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a
promising technology to assist future wireless communications due to its flexible, swift and …
promising technology to assist future wireless communications due to its flexible, swift and …
Deep reinforcement learning-based resource allocation in cooperative UAV-assisted wireless networks
We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
Deep reinforcement learning for trajectory design and power allocation in UAV networks
Unmanned aerial vehicle (UAV) is considered to be a key component in the next-generation
cellular networks. Considering the non-convex characteristic of the trajectory design and …
cellular networks. Considering the non-convex characteristic of the trajectory design and …
Multi-agent reinforcement learning-based resource allocation for UAV networks
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for
providing both cost-effective and on-demand wireless communications. This article …
providing both cost-effective and on-demand wireless communications. This article …
Trajectory design and resource allocation for multi-UAV networks: Deep reinforcement learning approaches
The future mobile communication system is expected to provide ubiquitous connectivity and
unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which …
unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which …
Machine learning-based resource allocation for multi-UAV communications system
Z Chang, W Guo, X Guo… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
The unmanned aerial vehicle (UAV)-based wireless communication system is prominent in
its flexibility and low cost for providing ubiquitous connectivity. In this work, considering a …
its flexibility and low cost for providing ubiquitous connectivity. In this work, considering a …
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 …
wireless communication service. We propose two algorithms for designing the trajectory of …
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- deep reinforcement trajectory planning
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