Dynamic Laser Inter-Satellite Link Scheduling Based on Federated Reinforcement Learning: An Asynchronous Hierarchical Architecture

G Wang, F Yang, J Song, Z Han - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The scheduling of the laser inter-satellite links (LISLs) can effectively decrease the energy
consumption by deactivating idle LISLs and reduce the network latency by decreasing the …

Multi-agent deep reinforcement learning for dynamic laser inter-satellite link scheduling

G Wang, F Yang, J Song, Z Han - GLOBECOM 2023-2023 …, 2023 - ieeexplore.ieee.org
Laser inter-satellite links (LISLs) enable longer-range communication and cross-satellite
dynamic links that bypass intermediate satellites. However, the utilization of narrow laser …

Optimization for Dynamic Laser Inter-Satellite Link Scheduling with Routing: A Multi-Agent Deep Reinforcement Learning Approach

G Wang, F Yang, J Song, Z Han - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Laser inter-satellite links (LISLs) have greatly extended communication distance between
satellites, allowing for establishment of dynamic links to reduce communication delay …

DeepISL: Joint Optimization of LEO Inter-Satellite Link Planning and Power Allocation via Parameterized Deep Reinforcement Learning

Y Li, J Luo, Y Ran, J Pi - GLOBECOM 2023-2023 IEEE Global …, 2023 - ieeexplore.ieee.org
The Low Earth Orbit (LEO) satellite constellation has been recognized as an important
component of the future 6G network. Due to the high speed movement and limited on-board …

Dynamic planning of inter-plane inter-satellite links in LEO satellite networks

J Pi, Y Ran, H Wang, Y Zhao, R Zhao… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
Low Earth Orbit (LEO) satellite constellations are promising to provide global coverage and
low latency communication by deploying a large number of small satellites and widely …

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach

H Choi, S Pack - Sensors, 2022 - mdpi.com
In low earth orbit (LEO) satellite-based applications (eg, remote sensing and surveillance), it
is important to efficiently transmit collected data to ground stations (GS). However, LEO …

Machine learning-based resource allocation in satellite networks supporting internet of remote things

D Zhou, M Sheng, Y Wang, J Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Satellite networks have been regarded as a promising architecture for supporting the
Internet of remote things (IoRT) due to their advantages of wide coverage and high …

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 …

Federated Deep Reinforcement Learning Assisting TT&C Mission Scheduling in Mega Satellite Networks

J Feng, D Zhou, M Sheng, Y Zhu… - GLOBECOM 2023-2023 …, 2023 - ieeexplore.ieee.org
Satellite telemetry, tracking, and command (TT &C) operations are critical to ensuring the
normal operation of mega satellite networks. However, the distribution and number of …

Latency-Aware Data Allocation Optimization for LEO Satellite IoT Networks with Federated Learning

P Qin, D Xu, K Yu, A Al-Dulaimi… - GLOBECOM 2023-2023 …, 2023 - ieeexplore.ieee.org
Federated learning (FL) has been deployed on low earth orbit (LEO) satellites Internet of
Things (IoT), where learning models can be trained collaboratively, thus preserving IoT data …