Path planning for UAV communication networks: Related technologies, solutions, and opportunities
J Luo, Z Wang, M Xia, L Wu, Y Tian, Y Chen - ACM Computing Surveys, 2023 - dl.acm.org
Path planning has been a hot and challenging field in unmanned aerial vehicles (UAV). With
the increasing demand of society and the continuous progress of technologies, UAV …
the increasing demand of society and the continuous progress of technologies, UAV …
Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing
This article studies the trajectory control and task offloading (TCTO) problem in an
unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies …
unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies …
Toward smart traffic management with 3D placement optimization in UAV-assisted NOMA IIoT networks
ABM Adam, MSA Muthanna… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Next generation networks will involve huge number of industrial internet of things (IIoT)
sensors which require reliable connectivity with low latency to manage the data transmission …
sensors which require reliable connectivity with low latency to manage the data transmission …
A continuous actor–critic deep Q-learning-enabled deployment of UAV base stations: Toward 6G small cells in the skies of smart cities
N Parvaresh, B Kantarci - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base
stations, are considered to have promising potential to tackle the limitations of ground base …
stations, are considered to have promising potential to tackle the limitations of ground base …
Trajectory Planning and Resource Allocation for Multi-UAV Cooperative Computation
In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the
cooperative computation among multiple UAVs can improve the overall computation service …
cooperative computation among multiple UAVs can improve the overall computation service …
MO-AVC: Deep Reinforcement Learning Based Trajectory Control and Task Offloading in Multi-UAV Enabled MEC Systems
Z Gao, L Yang, Y Dai - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
We investigate the joint trajectory control and task offloading (JTCTO) problem in
multiunmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC). However …
multiunmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC). However …
Multipath planning acceleration method with double deep r-learning based on a genetic algorithm
E Palacios-Morocho, S Inca… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Autonomous navigation is a well-studied field in robotics requiring high standards of
efficiency and reliability. Many studies focus on applying AI techniques to obtain a high …
efficiency and reliability. Many studies focus on applying AI techniques to obtain a high …
JO-TADP: learning-based cooperative dynamic resource allocation for MEC–UAV-enabled wireless network
Providing robust communication services to mobile users (MUs) is a challenging task due to
the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing …
the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing …
Online path planning framework for UAV in rural areas
G Airlangga, A Liu - IEEE Access, 2022 - ieeexplore.ieee.org
A motion strategy plays an important role in supporting autonomous Unmanned Aerial
Vehicle (UAV) movement. Many studies have been conducted to improve the motion …
Vehicle (UAV) movement. Many studies have been conducted to improve the motion …
Cooperative Multi-Agent Deep Reinforcement Learning Methods for UAV-Aided Mobile Edge Computing Networks
This paper presents a cooperative multi-agent deep reinforcement learning (MADRL)
approach for unmmaned aerial vehicle (UAV)-aided mobile edge computing (MEC) …
approach for unmmaned aerial vehicle (UAV)-aided mobile edge computing (MEC) …