Dense Multi-Agent Reinforcement Learning Aided Multi-UAV Information Coverage for Vehicular Networks

H Fu, J Wang, J Chen, P Ren, Z Zhang… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
With the rapid development of wireless communication networks, UAVs serving as base
stations are increasingly being applied in various scenarios which not only include edge …

Multi-UAV coverage path planning: A distributed online cooperation method

W Hu, Y Yu, S Liu, C She, L Guo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) plays a significant role
in intelligent distributed surveillance systems. However, due to poor cooperation, most …

[HTML][HTML] Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach

Q Wu, Q Liu, Z Wu, J Zhang - Future Internet, 2023 - mdpi.com
In the field of ocean data monitoring, collaborative control and path planning of unmanned
aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In …

Transformer-based reinforcement learning for scalable multi-uav area coverage

D Chen, Q Qi, Q Fu, J Wang, J Liao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the
characteristics of flexible deployment and strong adaptability, which are an important …

Leveraging UAVs for coverage in cell-free vehicular networks: A deep reinforcement learning approach

M Samir, D Ebrahimi, C Assi… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
The success in transitioning towards smart cities relies on the availability of information and
communication technologies that meet the demands of this transformation. The terrestrial …

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 …

[HTML][HTML] 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 …

Multi-UAV assisted network coverage optimization for rescue operations using reinforcement learning

OS Oubbati, H Badis, A Rachedi… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
Mobile communication networks could make a significant difference in rescuing affected
people in post-disaster scenarios. However, the existing communication infrastructures tend …

Deployment and association of multiple UAVs in UAV-assisted cellular networks with the knowledge of statistical user position

L Wang, H Zhang, S Guo, D Yuan - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Exploiting unmanned aerial vehicles (UAVs) as flying relays is becoming an indispensable
strategy to assist terrestrial cellular networks to enhance coverage. One challenging …

Deep reinforcement learning based UAVs trajectory optimization for maximum communication coverage of users

Y Zhao, X Sun, Y Liu, Q Yang - 2022 4th International …, 2022 - ieeexplore.ieee.org
With its flexibility and easy deployment, UAV can timely provide communication for areas
with damaged infrastructure. However, due to some constraints of the UAV itself, such as …