Reinforcement learning for decentralized trajectory design in cellular UAV networks with sense-and-send protocol
Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing
applications over cellular networks. The performance of a UAV is determined by both its …
applications over cellular networks. The performance of a UAV is determined by both its …
Reinforcement learning for a cellular internet of UAVs: Protocol design, trajectory control, and resource management
Unmanned aerial vehicles (UAVs) can be powerful Internet of Things components to
execute sensing tasks over the next-generation cellular networks, which are generally …
execute sensing tasks over the next-generation cellular networks, which are generally …
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 …
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 …
Cooperative internet of UAVs: Distributed trajectory design by multi-agent deep reinforcement learning
Due to the advantages of flexible deployment and extensive coverage, unmanned aerial
vehicles (UAVs) have significant potential for sensing applications in the next generation of …
vehicles (UAVs) have significant potential for sensing applications in the next generation of …
Reinforcement learning in multiple-UAV networks: Deployment and movement design
A novel framework is proposed for quality of experience driven deployment and dynamic
movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex …
movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex …
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 …
Path design for cellular-connected UAV with reinforcement learning
This paper studies the path design problem for cellular-connected unmanned aerial vehicle
(UAV), which aims to minimize its mission completion time while maintaining good …
(UAV), which aims to minimize its mission completion time while maintaining good …
Intelligent trajectory design in UAV-aided communications with reinforcement learning
In this correspondence paper, we focus on a cellular network aided an unmanned aerial
vehicle (UAV) that serves as an aerial base station for multiple ground users. The UAV's …
vehicle (UAV) that serves as an aerial base station for multiple ground users. The UAV's …
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 …
wireless connections for ground users. Due to their constraints on both mobility and energy …
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- deep reinforcement trajectory planning
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- reinforcement learning path design