Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network

Q Zhang, L Zhu, Y Chen, S Jiang - Sensors, 2023 - mdpi.com
To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G
communications, satellite plays a more significant role in it. As users and devices explosively …

Energy-efficient traffic offloading for RSMA-based hybrid satellite terrestrial networks with deep reinforcement learning

Q Zhang, L Zhu, Y Chen, S Jiang - China Communications, 2024 - ieeexplore.ieee.org
As the demands of massive connections and vast coverage rapidly grow in the next wireless
communication networks, rate splitting multiple access (RSMA) is considered to be the new …

Deep Reinforcement Learning Based Resource Allocation for RSMA in LEO Satellite-Terrestrial Networks

J Huang, Y Yang, J Lee, D He… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This paper considers the joint optimization of resource allocation and power control for rate-
splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks …

Deep reinforcement learning-based power allocation for rate-splitting multiple access in 6G LEO satellite communication system

J Huang, Y Yang, L Yin, D He… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
Rate-splitting multiple access (RSMA) softly reconciles and decodes the extreme
interference by non-orthogonal transmission, which can remarkably solve the spectrum …

Multi-agent DRL for user association and power control in terrestrial-satellite network

X Li, H Zhang, W Li, K Long - 2021 IEEE global …, 2021 - ieeexplore.ieee.org
In the past few years, satellite communications have greatly affected our daily lives. Because
the resources of terrestrial-satellite network are limited, how to allocate resources of …

Multi-agent DRL for resource allocation and cache design in terrestrial-satellite networks

X Li, H Zhang, H Zhou, N Wang, K Long… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
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 …

Deep reinforcement learning for RSMA-based multi-functional wireless networks

SA Naser, AS Ali, S Muhaidat - GLOBECOM 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
The upcoming sixth generation (6G) is expected to support a wide range of applications that
require efficient sensing, accurate localization, and reliable communication capabilities …

Sum-rate maximization of RSMA-based aerial communications with energy harvesting: A reinforcement learning approach

J Seong, M Toka, W Shin - IEEE Wireless Communications …, 2023 - ieeexplore.ieee.org
In this letter, we investigate a joint power and beamforming design problem for rate-splitting
multiple access (RSMA)-based aerial communications with energy harvesting, where a self …

Energy-efficient rate-splitting multiple access: A deep reinforcement learning-based framework

M Diamanti, G Kapsalis, EE Tsiropoulou… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Rate-Splitting Multiple Access (RSMA) has been recognized as an effective technique to
reconcile the tradeoff between decoding interference and treating interference as noise in …

Dynamic power allocation in high throughput satellite communications: A two-stage advanced heuristic learning approach

X Hu, Y Wang, Z Liu, X Du, W Wang… - Ieee Transactions on …, 2022 - ieeexplore.ieee.org
The dynamic radio resource management technology is an essential technology in high
throughput satellite (HTS) communications. Aiming at the problem that the traditional static …