Trajectory optimization for autonomous flying base station via reinforcement learning
H Bayerlein, P De Kerret… - 2018 IEEE 19th …, 2018 - ieeexplore.ieee.org
In this work, we study the optimal trajectory of an unmanned aerial vehicle (UAV) acting as a
base station (BS) to serve multiple users. Considering multiple flying epochs, we leverage …
base station (BS) to serve multiple users. Considering multiple flying epochs, we leverage …
Energy minimization for cellular-connected UAV: From optimization to deep reinforcement learning
Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral
components of future cellular networks. To this end, one of the important problems to …
components of future cellular networks. To this end, one of the important problems to …
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 …
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 …
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 …
Multi-UAV dynamic wireless networking with deep reinforcement learning
This letter investigates a novel unmanned aerial vehicle (UAV)-enabled wireless
communication system, where multiple UAVs transmit information to multiple ground …
communication system, where multiple UAVs transmit information to multiple ground …
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 …
Optimal UAV base station trajectories using flow-level models for reinforcement learning
Cellular base stations (BS) and remote radio heads can be mounted on unmanned aerial
vehicles (UAV) for flexible, traffic-aware deployment. These UAV base station networks …
vehicles (UAV) for flexible, traffic-aware deployment. These UAV base station networks …
Trajectory optimization of flying energy sources using q-learning to recharge hotspot uavs
Despite the increasing popularity of commercial usage of UAVs or drone-delivered services,
their dependence on the limited-capacity on-board batteries hinders their flight-time and …
their dependence on the limited-capacity on-board batteries hinders their flight-time and …
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 …
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- reinforcement learning trajectory optimization
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