Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches
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 …
research interest in recent years and are gradually being utilized in various aspects of our …
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 …
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 …
Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …
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 …
UAV path planning for wireless data harvesting: A deep reinforcement learning approach
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation
communication networks requires efficient trajectory planning methods. We propose a new …
communication networks requires efficient trajectory planning methods. We propose a new …
AI-enabled UAV communications: Challenges and future directions
Recently, unmanned aerial vehicles (UAVs) communications gained significant
concentration as a talented technology for future wireless communications using its …
concentration as a talented technology for future wireless communications using its …
[HTML][HTML] A survey on applications of reinforcement learning in flying ad-hoc networks
Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …
Learning-based UAV path planning for data collection with integrated collision avoidance
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks,
and determining collision-free trajectory in multi-UAV noncooperative scenarios while …
and determining collision-free trajectory in multi-UAV noncooperative scenarios while …
Deep reinforcement learning for trajectory path planning and distributed inference in resource-constrained UAV swarms
The deployment flexibility and maneuverability of unmanned aerial vehicles (UAVs)
increased their adoption in various applications, such as wildfire tracking, border monitoring …
increased their adoption in various applications, such as wildfire tracking, border monitoring …