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 …

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 …

Multi-agent deep reinforcement learning for trajectory design and power allocation in multi-UAV networks

N Zhao, Z Liu, Y Cheng - IEEE Access, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV) is regarded as an effective technology in future wireless
networks. However, due to the non-convexity feature of joint trajectory design and power …

Deep reinforcement learning for trajectory design and power allocation in UAV networks

N Zhao, Y Cheng, Y Pei, YC Liang… - ICC 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
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 …

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 …

Multi-agent reinforcement learning-based resource allocation for UAV networks

J Cui, Y Liu, A Nallanathan - IEEE Transactions on Wireless …, 2019 - ieeexplore.ieee.org
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 …

Deep reinforcement learning-based resource allocation in cooperative UAV-assisted wireless networks

P Luong, F Gagnon, LN Tran… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …

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 …

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 …

Cellular UAV-to-device communications: Trajectory design and mode selection by multi-agent deep reinforcement learning

F Wu, H Zhang, J Wu, L Song - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial
vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed …