A deep reinforcement learning based distributed multi-UAV dynamic area coverage algorithm for complex environment
J Xiao, G Yuan, Y Xue, J He, Y Wang, Y Zou, Z Wang - Neurocomputing, 2024 - Elsevier
Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-
UAV) systems to realize searching or monitoring an area of interest (AOI). Improving …
UAV) systems to realize searching or monitoring an area of interest (AOI). Improving …
Reinforcement learning for improved UAV-based integrated access and backhaul operation
There is a strong interest in utilizing commercial cellular networks to support unmanned
aerial vehicles (UAVs) to send control commands and communicate heavy traffic. Cellular …
aerial vehicles (UAVs) to send control commands and communicate heavy traffic. Cellular …
Learning-based Dynamic 3D Placement of Multiple Aerial Base Stations with Heterogeneous Traffic
Deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (ABSs) has
emerged as a promising solution to enable high-quality services in next-generation mobile …
emerged as a promising solution to enable high-quality services in next-generation mobile …
Energy-efficient multi-uavs cooperative trajectory optimization for communication coverage: An madrl approach
T Ao, K Zhang, H Shi, Z Jin, Y Zhou, F Liu - Remote Sensing, 2023 - mdpi.com
Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which
dynamically cover the wireless communication networks for Ground Users (GUs). The most …
dynamically cover the wireless communication networks for Ground Users (GUs). The most …
Multi-UAV Trajectory, Resource Allocation Design for UAV-Based Wireless Networks with Dynamic Data Demand for Consecutive Service Periods
Most existing designs for UAV-based wireless networks consider individual service periods;
therefore, they may not efficiently utilize the UAV energy and radio resources to serve users …
therefore, they may not efficiently utilize the UAV energy and radio resources to serve users …
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 …
Deploying uav base stations in communication network using machine learning
X Zhong - 2019 - dspace.library.uvic.ca
Today has witnessed a constantly increasing demand for high-quality wireless
communications services. Moreover, the quality of service (QoS) requirement of future 5G …
communications services. Moreover, the quality of service (QoS) requirement of future 5G …
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 …
Rate Splitting Enabled Uplink-Downlink Decoupled Association in UAV-Assisted Cellular Networks
In this paper, we study the performance of uplink-downlink decoupled (UDDe) user
association in unmanned aerial vehicles (UAVs)-assisted cellular networks in which each …
association in unmanned aerial vehicles (UAVs)-assisted cellular networks in which each …
Deep Reinforcement Learning-enabled Dynamic UAV Deployment and Power Control in Multi-UAV Wireless Networks
Using Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing services to
ground users has received growing research interest in recent years. The dynamic …
ground users has received growing research interest in recent years. The dynamic …