Seamless and intelligent resource allocation in 6G maritime networks framework via deep reinforcement learning

SS Hassan, SB Park, EN Huh… - 2023 International …, 2023 - ieeexplore.ieee.org
Sixth-generation (6G) communication networks will fulfill users' requests for high data
speeds and low latency without causing network outages throughout the world. However …

Multi-agent DRL-based task offloading in hierarchical HAP-LAP networks

TH Nguyen, L Park - 2022 13th International Conference on …, 2022 - ieeexplore.ieee.org
Future wireless networks promise to offer ubiq-uity connection to numerous Internet of
Things devices with various demands. Aerial access networks that combine satellite and …

Two-timescale learning-based task offloading for remote IoT in integrated satellite–terrestrial networks

D Han, Q Ye, H Peng, W Wu, H Wu… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to
support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite …

[HTML][HTML] HAP-assisted multi-aerial base station deployment for capacity enhancement via federated deep reinforcement learning

L Liu, H He, F Qi, Y Zhao, W Xie, F Zhou… - Journal of Cloud …, 2023 - Springer
Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are
widely employed in cloud computing, disaster relief, and various applications. How to …

Deep reinforcement learning based power allocation for high throughput satellites

N Dai, D Zhou, M Sheng, J Li - 2021 IEEE 94th Vehicular …, 2021 - ieeexplore.ieee.org
Non-terrestrial network (NTN) communication is included in the 3GPP standard because of
its excellent features such as resistance to ground physical attacks and wide coverage …

[HTML][HTML] Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning

D Wei, C Guo, L Yang - Sensors, 2023 - mdpi.com
Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are
unevenly distributed in the constantly changing coverage area, and the service …

Computing offloading and resource scheduling based on DDPG in ultra-dense edge computing networks

R Du, J Wang, Y Gao - The Journal of Supercomputing, 2024 - Springer
To address the current challenge of smart devices in healthcare Internet of things (IoT)
struggling to efficiently process intensive applications in real-time, a collaborative cloud …

Resource scheduling in satellite networks: A sparse representation based machine learning approach

C Bao, D Zhou, M Sheng, Y Shi… - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
With the growth of global communication service demand, constructing large-scale satellite
networks has become the future development trend for improved system performance …

A novel deep reinforcement learning architecture for dynamic power and bandwidth allocation in multibeam satellites

J Xu, Z Zhao, L Wang, Y Zhang - Acta Astronautica, 2023 - Elsevier
Due to the explosive growth and dynamic change of user demand, an efficient power and
bandwidth allocation algorithm is quite essential for multibeam satellites with flexible digital …

[HTML][HTML] LEO-Assisted Aerial Deployment in Post-Disaster Scenarios Using a Combinatorial Bandit and Genetic Algorithmic Approach

EM Mohamed, S Hashima, K Hatano, HS Khallaf - Electronics, 2023 - mdpi.com
This paper proposes integrating low earth orbit satellites (LEO-Sats) and multiple aerials to
provide rescue services in post-disaster areas. Aerials are distributed to provide wireless …