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 …
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 …
HAPS-UAV-enabled heterogeneous networks: A deep reinforcement learning approach
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude
platform station (HAPS) and low-altitude platform station (LAPS) has become essential …
platform station (HAPS) and low-altitude platform station (LAPS) has become essential …
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 …
Harnessing UAVs for fair 5G bandwidth allocation in vehicular communication via deep reinforcement learning
Terrestrial infrastructure-based wireless networks do not always guarantee their resources
will be shared uniformly by nodes in vehicular networks. This is due mainly to the uneven …
will be shared uniformly by nodes in vehicular networks. This is due mainly to the uneven …
Seamless and energy-efficient maritime coverage in coordinated 6G space–air–sea non-terrestrial networks
Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial
systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of …
systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of …
Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-
range communication with low latency. Integrating this with burgeoning unmanned aerial …
range communication with low latency. Integrating this with burgeoning unmanned aerial …
Deep Reinforcement Learning‐Based Joint Satellite Scheduling and Resource Allocation in Satellite‐Terrestrial Integrated Networks
Y Yin, C Huang, DF Wu, S Huang… - Wireless …, 2022 - Wiley Online Library
Satellite‐terrestrial integrated networks (STINs) are considered to be a new paradigm for the
next generation of global communication because of its distinctive merits, such as wide …
next generation of global communication because of its distinctive merits, such as wide …
Distributed UAV-BSs trajectory optimization for user-level fair communication service with multi-agent deep reinforcement learning
Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless
communication due to its agility and altitude. UAVs can be used as low-altitude aerial base …
communication due to its agility and altitude. UAVs can be used as low-altitude aerial base …
5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul
Fast and reliable wireless communication has become a critical demand in human life. In the
case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing …
case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing …
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