Multi-Agent Deep Reinforcement Learning for Dynamic Laser Inter-Satellite Link Scheduling-Reference-Cited by-同舟云学术

Multi-Agent Deep Reinforcement Learning for Dynamic Laser Inter-Satellite Link Scheduling

Published:2023-12-04 Issue: Volume: Page:5751-5756
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Wang Guanhua¹,Yang Fang¹,Song Jian¹,Han Zhu²

Affiliation:

1. Tsinghua University,Beijing National Research Center for Information Science and Technology (BNRist),Department of Electronic Engineering,Beijing,P. R. China,100084

2. University of Houston,Department of Electrical and Computer Engineering,Houston,TX,USA,77004

Funder

National Key Research and Development Program of China

Science, Technology and Innovation Commission of Shenzhen Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437721.pdf?arnumber=10437721

Reference18 articles.

1. Delay is Not an Option

2. Optical inter-satellite link terminals for next generation satellite constellations

3. Laser Intersatellite Links in a Starlink Constellation: A Classification and Analysis

4. Temporary Laser Inter-Satellite Links in Free-Space Optical Satellite Networks

5. Towards Energy-Efficient Routing in Satellite Networks

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2. Optimization for Dynamic Laser Inter-Satellite Link Scheduling with Routing: A Multi-Agent Deep Reinforcement Learning Approach;IEEE Transactions on Communications;2024