Reinforcement learning in the sky: A survey on enabling intelligence in ntn-based communications
Non terrestrial networks (NTN) involving 'in the sky'objects such as low-earth orbit satellites,
high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected …
high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected …
Continuous maneuver control and data capture scheduling of autonomous drone in wireless sensor networks
Thanks to flexible deployment and excellent maneuverability, autonomous drones are
regarded as an effective means to enable aerial data capture in large-scale wireless sensor …
regarded as an effective means to enable aerial data capture in large-scale wireless sensor …
Fairness-aware link optimization for space-terrestrial integrated networks: A reinforcement learning framework
The integration of space and air components considering satellites and unmanned aerial
vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has …
vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has …
Path planning for the dynamic uav-aided wireless systems using monte carlo tree search
For UAV-aided wireless systems, online path planning attracts much attention recently. To
better adapt to the real-time dynamic environment, for the first time, we propose a Monte …
better adapt to the real-time dynamic environment, for the first time, we propose a Monte …
Re-envisioning space-air-ground integrated networks: Reinforcement learning for link optimization
To provide ubiquitous connectivity and achieve high reliability in the under-served and
under-connected areas, the integration of aerial and space communication infrastructures …
under-connected areas, the integration of aerial and space communication infrastructures …
Reinforcement learning aided UAV base station location optimization for rate maximization
SP Gopi, M Magarini - Electronics, 2021 - mdpi.com
The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining
popularity. In this paper, we consider maximization of the overall data rate by intelligent …
popularity. In this paper, we consider maximization of the overall data rate by intelligent …
Deep q-learning for two-hop communications of drone base stations
In this paper, we address the application of the flying Drone Base Stations (DBS) in order to
improve the network performance. Given the high degrees of freedom of a DBS, it can …
improve the network performance. Given the high degrees of freedom of a DBS, it can …
Learning in the sky: Towards efficient 3D placement of UAVs
Deployment of unmanned aerial vehicles (UAVs) as aerial base stations to support cellular
networks can deliver a fast and flexible solution for serving high and varying traffic demand …
networks can deliver a fast and flexible solution for serving high and varying traffic demand …
UAV-assisted space-air-ground integrated networks: A technical review of recent learning algorithms
Recent technological advancements in space, air and ground components have made
possible a new network paradigm called" space-air-ground integrated network"(SAGIN) …
possible a new network paradigm called" space-air-ground integrated network"(SAGIN) …
Ground-to-UAV communication network: Stochastic geometry-based performance analysis
In this paper, we employ stochastic geometry to analyze ground-to-unmanned aerial vehicle
(UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with …
(UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with …