Distributed intelligence: A verification for multi-agent DRL-based multibeam satellite resource allocation
X Liao, X Hu, Z Liu, S Ma, L Xu, X Li… - IEEE …, 2020 - ieeexplore.ieee.org
Centralized radio resource management method puts all of the computational burdens in an
agent, which is unbearable with the increasing of data dimensionality. This letter focuses on …
agent, which is unbearable with the increasing of data dimensionality. This letter focuses on …
Multi-agent deep reinforcement learning-based flexible satellite payload for mobile terminals
X Hu, X Liao, Z Liu, S Liu, X Ding… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Information dissemination in mobile networks turns out to be a problem when the network is
sparse. Mobile networks begin to establish a separate cluster attributable to the limited …
sparse. Mobile networks begin to establish a separate cluster attributable to the limited …
Multi-agent DRL for resource allocation and cache design in terrestrial-satellite networks
In the past few years, satellite communications have greatly affected our daily lives, and the
integrated terrestrial-satellite network can combine the advantages of satellite and base …
integrated terrestrial-satellite network can combine the advantages of satellite and base …
Dynamic resource allocation with deep reinforcement learning in multibeam satellite communication
D Deng, C Wang, M Pang… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
In this letter, the radio resource optimization in multibeam geostationary earth orbit (GEO)
satellite communication (Satcom) is studied. We propose a deep reinforcement learning …
satellite communication (Satcom) is studied. We propose a deep reinforcement learning …
Machine learning for radio resource management in multibeam GEO satellite systems
Satellite communications (SatComs) systems are facing a massive increase in traffic
demand. However, this increase is not uniform across the service area due to the uneven …
demand. However, this increase is not uniform across the service area due to the uneven …
Sequential dynamic resource allocation in multi-beam satellite systems: A learning-based optimization method
Y Huang, WU Shufan, Z Zhankui, K Zeyu… - Chinese Journal of …, 2023 - Elsevier
Multi-beam antenna and beam hopping technologies are an effective solution for scarce
satellite frequency resources. One of the primary challenges accompanying with Multi-Beam …
satellite frequency resources. One of the primary challenges accompanying with Multi-Beam …
The next generation heterogeneous satellite communication networks: Integration of resource management and deep reinforcement learning
This article proposes an innovative resource management framework for the next generation
heterogeneous satellite networks (HSNs), which can achieve cooperation between …
heterogeneous satellite networks (HSNs), which can achieve cooperation between …
Deep reinforcement learning architecture for continuous power allocation in high throughput satellites
In the coming years, the satellite broadband market will experience significant increases in
the service demand, especially for the mobility sector, where demand is burstier. Many of the …
the service demand, especially for the mobility sector, where demand is burstier. Many of the …
A deep reinforcement learning-based framework for dynamic resource allocation in multibeam satellite systems
X Hu, S Liu, R Chen, W Wang… - IEEE Communications …, 2018 - ieeexplore.ieee.org
Dynamic resource allocation (DRA) is the key technology to improve the network
performance in resource-limited multibeam satellite (MBS) systems. The aim is to find a …
performance in resource-limited multibeam satellite (MBS) systems. The aim is to find a …
Dynamic beam pattern and bandwidth allocation based on multi-agent deep reinforcement learning for beam hopping satellite systems
Due to the non-uniform geographic distribution and time-varying characteristics of the
ground traffic request, how to make full use of the limited beam resources to serve users …
ground traffic request, how to make full use of the limited beam resources to serve users …