Mobility aware and energy-efficient federated deep reinforcement learning assisted resource allocation for 5G-RAN slicing-Reference-Cited by-同舟云学术

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Mobility aware and energy-efficient federated deep reinforcement learning assisted resource allocation for 5G-RAN slicing

Published:2024-03 Issue: Volume:217 Page:166-182
ISSN:0140-3664
Container-title:Computer Communications
language:en
Short-container-title:Computer Communications

Author:

Azimi Yaser,Yousefi Saleh^ORCID,Kalbkhani Hashem,Kunz Thomas

Publisher

Elsevier BV

Reference44 articles.

1. Optimizing communication and computational resource allocations in network slicing using twin-GAN-Based DRL for 5G hybrid C-RAN;Chen;Comput. Commun.,2023

2. From 5G to 6G—Challenges, technologies, and applications;Salameh;Future Internet,2022

3. Nrflex: Enforcing network slicing in 5g new radio;Boutiba;Comput. Commun.,2022

4. Applications of machine learning in resource management for RAN-slicing in 5G and beyond networks: A survey;Azimi;IEEE Access,2022

5. Resource allocation for network slicing in dynamic multi-tenant networks: A deep reinforcement learning approach;Xie;Comput. Commun.,2022

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