[HTML][HTML] Communication-enabled deep reinforcement learning to optimise energy-efficiency in UAV-assisted networks
Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless
connectivity to static and mobile ground users in situations of increased network demand or …
connectivity to static and mobile ground users in situations of increased network demand or …
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 …
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to
provide wireless connectivity to mobile users, such as vehicles. However, the density of …
provide wireless connectivity to mobile users, such as vehicles. However, the density of …
Multi-agent deep reinforcement learning for optimising energy efficiency of fixed-wing UAV cellular access points
Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation
communications, as they can be deployed to provide wireless connectivity to ground users …
communications, as they can be deployed to provide wireless connectivity to ground users …
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 …
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 …
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to
provide wireless connectivity to ground devices in events of increased network demand …
provide wireless connectivity to ground devices in events of increased network demand …
Umix: Sustainable Multi-UAV Coordination for Aerial-Terrestrial Networks
The deployment of Unmanned Aerial Vehicles (UAVs) alongside wireless communication
networks as Aerial-Terrestrial Network (ATN) in vast and remote wilderness regions shows a …
networks as Aerial-Terrestrial Network (ATN) in vast and remote wilderness regions shows a …
Decentralized trajectory and power control based on multi-agent deep reinforcement learning in UAV networks
Unmanned aerial vehicles (UAVs) are capable of enhancing the coverage of existing
cellular networks by acting as aerial base stations (ABSs). Due to the limited on-board …
cellular networks by acting as aerial base stations (ABSs). Due to the limited on-board …
Adaptive deployment of UAV-aided networks based on hybrid deep reinforcement learning
X Ma, S Hu, D Zhou, Y Zhou… - 2020 IEEE 92nd Vehicular …, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless
connections for ground users. Due to their constraints on both mobility and energy …
connections for ground users. Due to their constraints on both mobility and energy …