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 …
UAV-assisted 5G/6G networks: Joint scheduling and resource allocation based on asynchronous reinforcement learning
Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) for providing
wireless communications and coverage enhancement in fifth/sixth-generation (5G/6G) …
wireless communications and coverage enhancement in fifth/sixth-generation (5G/6G) …
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 …
Multi-agent deep reinforcement learning based UAV trajectory optimization for differentiated services
Driven by the increasing computational demand of real-time mobile applications, Unmanned
Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as …
Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as …
Joint UAV Deployment and Resource Allocation: a Personalized Federated Deep Reinforcement Learning Approach
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for
providing dynamic coverage and connectivity extension for the sixth-generation (6G) …
providing dynamic coverage and connectivity extension for the sixth-generation (6G) …
Distributed UAV-BSs trajectory optimization for user-level fair communication service with multi-agent deep reinforcement learning
Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless
communication due to its agility and altitude. UAVs can be used as low-altitude aerial base …
communication due to its agility and altitude. UAVs can be used as low-altitude aerial base …
[HTML][HTML] Deep reinforcement learning for joint trajectory planning, transmission scheduling, and access control in UAV-assisted wireless sensor networks
Unmanned aerial vehicles (UAVs) can be used to relay sensing information and
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
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 …
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 …
[HTML][HTML] Communication-enabled deep reinforcement learning to optimise energy-efficiency in UAV-assisted networks
Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless
connectivity to static and mobile ground users in situations of increased network demand or …
connectivity to static and mobile ground users in situations of increased network demand or …
相关搜索
- deep reinforcement access control
- deep reinforcement trajectory planning
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- deep reinforcement transmission scheduling
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- deep reinforcement network formation
- deep reinforcement uav swarms
- uav networks power control