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 …
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 …
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
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 …
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 …
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 …
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 …
Bayesian optimization enhanced deep reinforcement learning for trajectory planning and network formation in multi-UAV networks
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 …
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
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 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
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 …
vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed …
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