A Deep Reinforcement Learning based Routing Scheme for LEO Satellite Networks in 6G

Author:

Hsu Yi-Huai1,Lee Jiun-Ian1,Xu Feng-Ming1

Affiliation:

1. Yuan Ze University,Department of Computer Science and Engineering,Taoyuan

Funder

Ministry of Science and Technology

Publisher

IEEE

Reference17 articles.

1. DRL-ER: An Intelligent Energy-Aware Routing Protocol With Guaranteed Delay Bounds in Satellite Mega-Constellations

2. An Intelligent Routing Algorithm for LEO Satellites Based on Deep Reinforcement Learning

3. Continuous control with deep reinforcement learning;lillicrap;International Conference on Learning Representations (ICLR),2016

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

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enabling High-Throughput Routing for LEO Satellite Broadband Networks: A Flow-Centric Deep Reinforcement Learning Approach;IEEE Internet of Things Journal;2024-09-01

2. DT-DDQNR: A Digital Twin Assisted Direct-to-Cell Satellite Network Intelligent Routing Algorithm;Advances in Space Research;2024-09

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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