Energy minimization for cellular-connected UAV: From optimization to deep reinforcement learning
Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral
components of future cellular networks. To this end, one of the important problems to …
components of future cellular networks. To this end, one of the important problems to …
Optimizing energy efficiency in UAV-assisted networks using deep reinforcement learning
In this letter, we study the energy efficiency (EE) optimization of unmanned aerial vehicles
(UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent …
(UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent …
Joint trajectory design and BS association for cellular-connected UAV: An imitation-augmented deep reinforcement learning approach
YJ Chen, DY Huang - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
This article concerns the problem of the trajectory design and base station (BS) association
for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions …
for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions …
[HTML][HTML] GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks
P Thantharate, A Thantharate, A Kulkarni - Green Energy and Intelligent …, 2024 - Elsevier
Abstract Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the
increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking …
increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking …
Reinforcement learning for energy-efficient trajectory design of UAVs
Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial
cellular networks has emerged as an effective solution to provide coverage and complement …
cellular networks has emerged as an effective solution to provide coverage and complement …
Cellular UAV-to-device communications: Trajectory design and mode selection by multi-agent deep reinforcement learning
In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial
vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed …
vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed …
Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning
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 …
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …
Energy-efficient UAV control for effective and fair communication coverage: A deep reinforcement learning approach
Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance
both the coverage and performance of communication networks in various scenarios, such …
both the coverage and performance of communication networks in various scenarios, such …
Multiagent Q-Learning-Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design
In air-to-ground communications, the network lifetime depends on the operation time of
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
Energy minimization in UAV-aided networks: Actor-critic learning for constrained scheduling optimization
In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage
have triggered the development of intelligent energy-conserving scheduling solutions. In this …
have triggered the development of intelligent energy-conserving scheduling solutions. In this …