An Efficient Reinforcement Learning-Based Cooperative Navigation Algorithm for Multiple UAVs in Complex Environments

L Zhang, W Yi, H Lin, J Peng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous navigation of multiple unmanned aerial vehicles (UAVs) serves as the
foundation for their widespread applications in various fields. However, multi-UAV …

Energy Maximization for Wireless Powered Communication Enabled IoT Devices With NOMA Underlaying Solar Powered UAV Using Federated Reinforcement …

A Jabbari, H Khan, S Duraibi, I Budhiraja… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The Internet of Things (IoT) depends primarily on low-cost wireless sensors with limited
energy capacity to allow pervasive monitoring and intelligent control. Nevertheless …

Optimum UAV Trajectory Design for Data Harvesting from Distributed Nodes

D Kudathanthirige, H Inaltekin… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
This paper designs energy-efficient trajectories for unmanned aerial vehicles (UAVs)
harvesting data sequentially from distributed ground nodes. We propose a novel …

Joint Optimization of Multi-UAV Deployment and User Association Via Deep Reinforcement Learning for Long-Term Communication Coverage

X Cheng, R Jiang, H Sang, G Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The flexible deployment and strong adaptability of unmanned aerial vehicles (UAVs) have
great significance in improving communication quality and expanding communication …

Multi-UAV clustered NOMA for covert communications: Joint resource allocation and trajectory optimization

X Qin, X Wu, M Xiong, Y Liu, Y Zhang - Electronics, 2022 - mdpi.com
Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-
assisted communication networks have been widely used in civil and military fields …

Hypergraph convolution mix DDPG for multi-aerial base station deployment

H He, F Zhou, Y Zhao, W Li, L Feng - Journal of Cloud Computing, 2023 - Springer
Aerial base stations (AeBS), as crucial components of air-ground integrated networks, can
serve as the edge nodes to provide flexible services to ground users. Optimizing the …

Environment and Energy-Aware AUV-Assisted Data Collection for the Internet of Underwater Things

Z Zhang, J Xu, G Xie, J Wang, Z Han… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Considering the wide-area distribution and limited transmission power of sensing devices in
the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles …

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

X Wang, Y Zhang, H Wu, T Liu, Y Xu - Physical Communication, 2022 - Elsevier
This paper proposes a resource allocation scheme for hybrid multiple access involving both
orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The …

DDPG-Based Optimization for Zero-Forcing Transmission in UAV-Relay Massive MIMO Networks

MTP Le, V Nguyen-Duy-Nhat… - IEEE Open Journal of …, 2024 - ieeexplore.ieee.org
This study explores the advantages of employing an unmanned aerial vehicle (UAV) in a
massive multiple-input multiple-output (MIMO) network with zero-forcing processing at the …

A Decision-Making System for Cotton Irrigation Based on Reinforcement Learning Strategy

Y Chen, Z Yu, Z Han, W Sun, L He - Agronomy, 2023 - mdpi.com
This article addresses the challenges of water scarcity and climate change faced by cotton
cultivation in the Xinjiang region of China. In response, a precise irrigation model based on …