Dynamic beam hopping method based on multi-objective deep reinforcement learning for next generation satellite broadband systems

X Hu, Y Zhang, X Liao, Z Liu, W Wang… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
When regarding the inherent uncertainty of differentiated services requirements as well as
the non-uniform spatial distribution of capacity requests, it is essential to flexibility adjust …

Dynamic beam pattern and bandwidth allocation based on multi-agent deep reinforcement learning for beam hopping satellite systems

Z Lin, Z Ni, L Kuang, C Jiang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

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 …

Deep reinforcement learning‐based beam Hopping algorithm in multibeam satellite systems

X Hu, S Liu, Y Wang, L Xu, Y Zhang… - IET …, 2019 - Wiley Online Library
Beam hopping (BH) is the key technology to improve the system throughput and decrease
the transmission delay in multibeam satellite systems. The objective of this study is to find a …

Deep reinforcement learning based dynamic channel allocation algorithm in multibeam satellite systems

S Liu, X Hu, W Wang - IEEE Access, 2018 - ieeexplore.ieee.org
Dynamic channel allocation (DCA) is the key technology to efficiently utilize the spectrum
resources and decrease the co-channel interference for multibeam satellite systems. Most …

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 …

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 …

Beam illumination pattern design in satellite networks: Learning and optimization for efficient beam hopping

LEI Lei, E Lagunas, Y Yuan, MG Kibria… - IEEE …, 2020 - ieeexplore.ieee.org
Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular
and time-varying traffic requests in future multi-beam satellite systems. In BH optimization …

The next generation heterogeneous satellite communication networks: Integration of resource management and deep reinforcement learning

B Deng, C Jiang, H Yao, S Guo… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
This article proposes an innovative resource management framework for the next generation
heterogeneous satellite networks (HSNs), which can achieve cooperation between …

Deep learning-based resource allocation for 5G broadband TV service

P Yu, F Zhou, X Zhang, X Qiu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The vision of next-generation TV is to support media services to achieve sharing of cross-
domain experience, and the eMBB scenario of the 5G network is one of its important driving …