A Reinforcement Learning Based Approach for 5G Network Slicing Across Multiple Domains-Reference-Cited by-同舟云学术

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A Reinforcement Learning Based Approach for 5G Network Slicing Across Multiple Domains

Published:2019-10 Issue: Volume: Page:
ISSN:
Container-title:2019 15th International Conference on Network and Service Management (CNSM)
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Author:

Kibalya Godfrey,Serrat Joan,Gorricho Juan-Luis,Pasquini Rafael,Yao Haipeng,Zhang Peiying

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8977268/9012657/09012674.pdf?arnumber=9012674

Cited by 17 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. Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning;Future Generation Computer Systems;2024-09

2. Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning;International Journal of Electrical and Electronics Research;2024-05-30

3. Multi-Resource Network Slicing with Deep Reinforcement Learning for an Optimal QoS Satisfaction Ratio;2024 16th International Conference on Advanced Computational Intelligence (ICACI);2024-05-16

4. An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks;Neural Computing and Applications;2024-03-28

5. Multi-Domain Network Service Placement Optimization Using Curriculum Reinforcement Learning;2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN);2023-11-07

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