Onboard deep deterministic policy gradients for online flight resource allocation of UAVs

K Li, Y Emami, W Ni, E Tovar, Z Han - IEEE Networking Letters, 2020 - ieeexplore.ieee.org
In Unmanned Aerial Vehicle (UAV) enabled data collection, scheduling data transmissions
of the ground nodes while controlling flight of the UAV, eg, heading and velocity, is critical to …

Joint flight cruise control and data collection in UAV-aided Internet of Things: An onboard deep reinforcement learning approach

K Li, W Ni, E Tovar, M Guizani - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things
(IoT) networks is a promising technology for large-scale environment sensing. A key …

LSTM-characterized deep reinforcement learning for continuous flight control and resource allocation in UAV-assisted sensor network

K Li, W Ni, F Dressler - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote
wireless sensor networks (WSNs). Due to UAV's maneuvering, scheduling a sensor device …

Deep-reinforcement-learning-based optimal transmission policies for opportunistic UAV-aided wireless sensor network

Y Liu, J Yan, X Zhao - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
When there are unmanned aerial vehicles (UAVs) performing their specifically assigned
tasks in the air, some of them still have available resources to access different ground …

Joint Optimization of Trajectory Control, Resource Allocation and User Association Based on DRL for Multi-Fixed-Wing UAV Networks

B Yin, X Fang, X Wang - IEEE Transactions on Wireless …, 2024 - ieeexplore.ieee.org
Owing to the abundance of onboard energy and wide coverage, fixed-wing unmanned
aerial vehicles (FW-UAVs) have better capabilities to serve as aerial base stations, thereby …

Data-driven deep reinforcement learning for online flight resource allocation in uav-aided wireless powered sensor networks

K Li, W Ni, H Kurunathan… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
In wireless powered sensor networks (WPSN), data of ground sensors can be collected or
relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be …

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 …

AI-based mobility-aware energy efficient resource allocation and trajectory design for NFV enabled aerial networks

M Pourghasemian, MR Abedi… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
In this paper, we propose a novel joint intelligent trajectory design and resource allocation
algorithm based on users' mobility and their requested services for unmanned aerial …

Trajectory design and bandwidth assignment for UAVs-enabled communication network with multi-agent deep reinforcement learning

W Wang, Y Lin - 2021 IEEE 94th Vehicular Technology …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV) is considered as a promising technique to enhance future
wireless mobile communication. In this paper, the UAVs serve as aerial base stations …

Trajectory design and access control for air–ground coordinated communications system with multiagent deep reinforcement learning

R Ding, Y Xu, F Gao, X Shen - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Unmanned-aerial-vehicle (UAV)-assisted communications has attracted increasing attention
recently. This article investigates air–ground coordinated communications system, in which …