Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning

Published:2023-12-04 Issue: Volume: Page:7145-7150
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Doan Khai¹,Avgeris Marios¹,Leivadeas Aris²,Lambadaris Ioannis¹,Shin Wonjae³

Affiliation:

1. Carleton University,Department of Systems and Computer Engineering,Ottawa,ON,Canada

2. École de Technologie Supérieure,Department of Software and IT Engineering,Montreal,QC,Canada

3. Korea University, School of Electrical Engineering,Seoul,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437296.pdf?arnumber=10437296

Reference9 articles.

1. Dynamic Resource Allocation for Virtual Network Function Placement in Satellite Edge Clouds

2. VNF Placement Optimization at the Edge and Cloud †

3. Survey of computer vision–based natural disaster warning systems

4. Dynamic Resource Management for Neighbor-based VNF Placement in Decentralized Satellite Networks

5. Virtual Network Function Placement in Satellite Edge Computing With a Potential Game Approach

Cited by 1 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. Joint Drone Access and LEO Satellite Backhaul for a Space–Air–Ground Integrated Network: A Multi-Agent Deep Reinforcement Learning-Based Approach;Drones;2024-05-25

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