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 …
Empowering non-terrestrial networks with artificial intelligence: A survey
6G networks can support global, ubiquitous and seamless connectivity through the
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …
convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios …
Collaborative computing in non-terrestrial networks: A multi-time-scale deep reinforcement learning approach
Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks
(NTNs) has been the most promising paradigm to enable global coverage. The limited …
(NTNs) has been the most promising paradigm to enable global coverage. The limited …
Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches
Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …
research interest in recent years and are gradually being utilized in various aspects of our …
Integrating LEO satellites and multi-UAV reinforcement learning for hybrid FSO/RF non-terrestrial networks
Integrating low-altitude earth orbit (LEO) satellites (SATs) and unmanned aerial vehicles
(UAVs) within a non-terrestrial network (NTN), we investigate the problem of forwarding …
(UAVs) within a non-terrestrial network (NTN), we investigate the problem of forwarding …
Reinforcement learning for satellite communications: From LEO to deep space operations
PVR Ferreira, R Paffenroth… - IEEE …, 2019 - ieeexplore.ieee.org
The National Aeronautics and Space Administration (NASA) is in the midst of defining and
developing the future space and ground architecture for the coming decades to return …
developing the future space and ground architecture for the coming decades to return …
Deep reinforcement learning for multi-user access control in non-terrestrial networks
Non-Terrestrial Networks (NTNs) composed of space-borne (eg, satellites) and airborne
vehicles (eg, drones and blimps) have recently been proposed by 3GPP as a new paradigm …
vehicles (eg, drones and blimps) have recently been proposed by 3GPP as a new paradigm …
DeepWiERL: Bringing deep reinforcement learning to the internet of self-adaptive things
F Restuccia, T Melodia - IEEE INFOCOM 2020-IEEE …, 2020 - ieeexplore.ieee.org
Recent work has demonstrated that cutting-edge advances in deep reinforcement learning
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …
Artificial intelligence for satellite communication: A review
F Fourati, MS Alouini - Intelligent and Converged Networks, 2021 - ieeexplore.ieee.org
Satellite communication offers the prospect of service continuity over uncovered and under-
covered areas, service ubiquity, and service scalability. However, several challenges must …
covered areas, service ubiquity, and service scalability. However, several challenges must …
Reliable backhauling in aerial communication networks against UAV failures: A deep reinforcement learning approach
Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish
wireless communication networks in various challenging scenarios, such as emergency …
wireless communication networks in various challenging scenarios, such as emergency …