A reinforcement learning approach for fair user coverage using UAV mounted base stations under energy constraints
HV Abeywickrama, Y He, E Dutkiewicz… - IEEE Open Journal …, 2020 - ieeexplore.ieee.org
Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless
communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and …
communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and …
Tactical UAV path optimization under radar threat using deep reinforcement learning
MN Alpdemir - Neural Computing and Applications, 2022 - Springer
The majority of the research efforts that aim to solve UAV path optimization problems in a
Reinforcement Learning (RL) setting focus on closed spaces or urban areas as the …
Reinforcement Learning (RL) setting focus on closed spaces or urban areas as the …
Joint communication scheduling and velocity control in multi-UAV-assisted sensor networks: A deep reinforcement learning approach
Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect
data from ground sensors in remote and hostile areas. A key challenge is the joint design of …
data from ground sensors in remote and hostile areas. A key challenge is the joint design of …
Swarm intelligence based robotic search in unknown maze-like environments
KAR Youssefi, M Rouhani - Expert Systems with Applications, 2021 - Elsevier
This paper proposes a novel decentralize and asynchronous robotic search algorithm based
on particle swarm optimization (PSO), which has focused on solving mazes and finding …
on particle swarm optimization (PSO), which has focused on solving mazes and finding …
Three-dimensional continuous movement control of drone cells for energy-efficient communication coverage
This paper is concerned with the efficient movement control of multiple drone cells for
communication coverage. Although many works have been developed to cope with this …
communication coverage. Although many works have been developed to cope with this …
Trajectory optimization of flying energy sources using q-learning to recharge hotspot uavs
Despite the increasing popularity of commercial usage of UAVs or drone-delivered services,
their dependence on the limited-capacity on-board batteries hinders their flight-time and …
their dependence on the limited-capacity on-board batteries hinders their flight-time and …
A novel hybrid discrete grey wolf optimizer algorithm for multi-UAV path planning
With the development of the fifth-generation wireless network, autonomous moving platforms
such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In …
such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In …
Internet of Low-Altitude UAVs (IoLoUA): a methodical modeling on integration of Internet of “Things” with “UAV” possibilities and tests
A Srivastava, J Prakash - Artificial Intelligence Review, 2023 - Springer
Evidence of the IoT is expanding the number of connected devices, including UAVs. UAVs
overcome the flaws in the physical IoT infrastructure already in place. Low-altitude views are …
overcome the flaws in the physical IoT infrastructure already in place. Low-altitude views are …
Caching to the sky: Performance analysis of cache-assisted CoMP for cellular-connected UAVs
Providing connectivity to aerial users, such as cellular-connected unmanned aerial vehicles
(UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the …
(UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the …
REQIBA: Regression and deep Q-learning for intelligent UAV cellular user to base station association
Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation
cellular networks. By operating in the sky, UAV users experience very different radio …
cellular networks. By operating in the sky, UAV users experience very different radio …