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Optimizing Energy Consumption in 5G HetNets: A Coordinated Approach for Multi-Level Picocell Sleep Mode with Q-Learning

By
Macoumba Fall ,
Macoumba Fall

IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Mohammed Fattah ,
Mohammed Fattah

IMAGE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Mohammed Mahfoudi ,
Mohammed Mahfoudi

ISE Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco

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Younes Balboul ,
Younes Balboul

IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Said Mazer ,
Said Mazer

IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Moulhime El Bekkali ,
Moulhime El Bekkali

IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Ahmed D. Kora ,
Ahmed D. Kora

EDMI, Cheikh Anta Diop University, Dakar, Senegal

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Abstract

Cell standby, particularly picocell sleep mode (SM), is a prominent strategy for reducing energy consumption in 5G networks. The emergence of multi-state sleep states necessitates new optimization approaches. This paper proposes a novel energy optimization strategy for 5G heterogeneous networks (HetNets) that leverages macrocell-picocell coordination and machine learning. The proposed strategy focuses on managing the four available picocell sleep states. The picocell manages the first three states using the Q-learning algorithm, an efficient reinforcement learning technique. The associated macrocell based on picocell energy efficiency controls the final, deeper sleep state. This hierarchical approach leverages localized and network-wide control strengths for optimal energy savings. By capitalizing on macrocell-picocell coordination and machine learning, this work presents a promising solution for achieving significant energy reduction in 5G HetNets while maintaining network performance.

How to Cite

1.
Fall M, Fattah M, Mahfoudi M, Balboul Y, Mazer S, El Bekkali M, Kora AD. Optimizing Energy Consumption in 5G HetNets: A Coordinated Approach for Multi-Level Picocell Sleep Mode with Q-Learning. Data and Metadata [Internet]. 2024 May 20 [cited 2024 Jun. 24];3:333. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/333

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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