Hierarchical Meta-Reinforcement Learning for Resource-Efficient Slicing in O-RAN-Reference-Cited by-同舟云学术

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Hierarchical Meta-Reinforcement Learning for Resource-Efficient Slicing in O-RAN

Published:2023-12-04 Issue: Volume: Page:2729-2735
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Chen Xianfu¹,Wu Celimuge²,Zhao Zhifeng³,Xiao Yong⁴,Mao Shiwen⁵,Ji Yusheng⁶

Affiliation:

1. VTT Technical Research Centre of Finland Ltd,Oulu,Finland

2. The University of Electro-Communications,Tokyo,Japan

3. Zhejiang Lab,Hangzhou,China

4. Huazhong University of Science and Technology,Wuhan,China

5. Auburn University,Auburn,AL,USA

6. National Institute of Informatics,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437350.pdf?arnumber=10437350

Reference19 articles.

1. Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

2. O-RAN Architecture Description - v7.0;Technical Specification,2022

3. SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems

4. QoS-Aware and Reliable Traffic Steering for Service Function Chaining in Mobile Networks

5. OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN

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