Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach-Reference-Cited by-同舟云学术

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Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Published:2024-10 Issue: Volume:66 Page:102386
ISSN:1874-4907
Container-title:Physical Communication
language:en
Short-container-title:Physical Communication

Author:

Sun Xiaochuan,Qin Zhenteng,Zhang Qi,Li Yingqi^ORCID

Publisher

Elsevier BV

Reference25 articles.

1. Reinforcement learning for radio resource management in RAN slicing: A survey;Zangooei;IEEE Commun. Mag.,2023

2. 5G wireless network slicing for eMBB, URLLC, and mMTC: A communication-theoretic view;Popovski;IEEE Access,2018

3. Artificial intelligence-assisted network slicing: Network assurance and service provisioning in 6G;Wang;IEEE Veh. Technol. Mag.,2023

4. Divide and conquer: A business model agenda for beyond-5G and 6G actors;Moussaoui;IEEE Commun. Mag.,2023

5. Connectivity maximization in non-orthogonal network slicing enabled industrial internet-of-things with multiple services;Yin;IEEE Trans. Wireless Commun.,2023

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