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Multi-Agent Deep Reinforcement Learning for Slicing and Admission Control in 5G C-RAN

  • Published:2022-04-25 Issue: Volume: Page:
  • ISSN:
  • Container-title:NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
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Author:

Sulaiman Muhammad1,Moayyedi Arash1,Salahuddin Mohammad A.1,Boutaba Raouf1,Saleh Aladdin2

Affiliation:

1. University of Waterloo,David R. Cheriton School of Computer Science,Ontario,Canada

2. Rogers Communications Inc.,Ontario,Canada

Publisher

IEEE

Reference33 articles.

1. PyTorch: An Imperative Style, High-Performance Deep Learning Library;paszke;Advances in Neural IInformation Processing Systems,2019

2. Tune: A Research Platform for Distributed Model Selection and Training;liaw,2018

3. RLlib: Abstractions for distributed reinforcement learning;liang;International Conference on Machine Learning,2018

4. OpenAI Gym;brockman,2016

5. Isolation-Aware 5G RAN Slice Mapping Over WDM Metro-Aggregation Networks

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1. Adaptive Methods for Revenue Model Learning of a Slice Broker in the Presence of Adversaries;Wireless Personal Communications;2024-05-10

2. Drift-Aware Policy Selection for Slice Admission Control;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06

3. Context-Based e2e Autonomous Operation in B5G Networks;Sensors;2024-03-01

4. Adaptive Admission Control for 6G Network Slicing Resource Allocation (A2C-NSRA);Lecture Notes on Data Engineering and Communications Technologies;2024

5. Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core;IEEE Communications Surveys & Tutorials;2024
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