A Deep Reinforcement Learning based Routing Scheme for LEO Satellite Networks in 6G
Author:
Affiliation:
1. Yuan Ze University,Department of Computer Science and Engineering,Taoyuan
Funder
Ministry of Science and Technology
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10118584/10118587/10118680.pdf?arnumber=10118680
Reference17 articles.
1. DRL-ER: An Intelligent Energy-Aware Routing Protocol With Guaranteed Delay Bounds in Satellite Mega-Constellations
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4. Bit Error Rate performance analysis for Free Space Optic communication;basudewa;IOP Conference Series: Materials Science and Engineering,2019
5. A Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning for LEO Satellite Networks
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3. SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization;IEEE Journal on Selected Areas in Communications;2024-05
4. Research on Distributed Routing Technology for LEO Satellite Network;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024
5. Enhancing Network Performance in LEO Satellite Networks through DRL-Based Routing Optimization;2023 Global Conference on Information Technologies and Communications (GCITC);2023-12-01
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