Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview

Published:2024-05-27 Issue: Volume:30 Page:1404-1411
ISSN:
Container-title:2024 International Wireless Communications and Mobile Computing (IWCMC)
language:
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Author:

Trabelsi Nessrine¹,Maaloul Rihab²,Fourati Lamia Chaari³,Jaafar Wael⁴

Affiliation:

1. Laboratory of Signals, systeMs, aRtificial intelligence, and neTworkS – Digital Research Center of Sfax,Sfax,Tunisia

2. University of Monastir,Higher Institute of Computer Science of Mahdia (ISIMa),Tunisia

3. University of Sfax,Higher Institute of Computer Science and Multimedia in Sfax (ISIMS),Tunisia

4. École de technologies supérieure (ÉTS),Department of Software and IT Engineering,Montreal,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10592105/10592309/10592464.pdf?arnumber=10592464

Reference37 articles.

1. Energy Optimization With Multi-Sleeping Control in 5G Heterogeneous Networks Using Reinforcement Learning

2. Cell On/Off Parameter Optimization for Saving Energy via Reinforcement Learning

3. An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

4. A review of machine learning techniques for enhanced energy efficient 5G and 6G communications

5. The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations

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