[HTML][HTML] Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

X Liu, W Zhong, X Wang, H Duan, Z Fan, H Jin… - Drones, 2024 - mdpi.com
To address the issue of limited application scenarios associated with connectivity assurance
based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep …

[HTML][HTML] Multi-Tier 3D Trajectory Planning for Cellular-Connected UAVs in Complex Urban Environments

X Luo, T Zhang, W Xu, C Fang, T Lu, J Zhou - Symmetry, 2023 - mdpi.com
Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address
communication and navigation limitations by leveraging base stations for air–ground …

A 3D REM‐Guided UAV Path Planning Method under Communication Connectivity Constraints

X Liu, L Zhou, X Zhang, X Tan… - … and Mobile Computing, 2022 - Wiley Online Library
With the emergence of a large number of smart devices, the radio environment in which
unmanned aerial vehicles (UAVs) take tasks is becoming more and more complex, which …

[HTML][HTML] Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach

W Zhong, X Wang, X Liu, Z Lin, F Ali - EURASIP Journal on Advances in …, 2024 - Springer
Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend
cellular services from the ground into the airspace, represent a promising technological …

Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning

Y Zeng, X Xu, S Jin, R Zhang - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …

Connectivity-aware 3D UAV path design with deep reinforcement learning

H Xie, D Yang, L Xiao, J Lyu - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
In this paper, we study the three-dimensional (3D) path planning problem for cellular-
connected unmanned aerial vehicle (UAV) taking into account the impact of 3D antenna …

Three-dimension trajectory design for multi-UAV wireless network with deep reinforcement learning

W Zhang, Q Wang, X Liu, Y Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The effective trajectory design of multiple unmanned aerial vehicles (UAVs) is investigated
for improving the capacity of the communication system. The aim is for maximizing real-time …

Multi-Agent Reinforcement Learning for UAVs 3D Trajectory Designing and Mobile Ground Users Scheduling with No-Fly Zones

Y Gao, S Wang, M Liu, Y Hu - 2023 IEEE/CIC International …, 2023 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV)-based aerial communication is considered a promising
technology in future wireless systems. In this paper, we study a multi-UAV-assisted data …

Cellular Network-Supported Machine Learning Techniques for Autonomous UAV Trajectory Planning

G Afifi, Y Gadallah - IEEE Access, 2022 - ieeexplore.ieee.org
Autonomous trajectory planning is a hot topic in the UAV mission planning area of research.
Autonomous UAVs have major use case applications which involve navigation in complex …

When digital twin meets deep reinforcement learning in multi-UAV path planning

S Li, X Lin, J Wu, AK Bashir, R Nawaz - Proceedings of the 5th …, 2022 - dl.acm.org
Unmanned aerial vehicles (UAVs) path planning is one of the promising technologies in the
fifth-generation wireless communications. The gap between simulation and reality limits the …