A Distributed Q-Learning Approach for Adaptive Sleep Modes in 5G Networks

Ali El Amine; Mauricio Iturralde; Hussein Al Haj Hassan; Loutfi Nuaymi

A Distributed Q-Learning Approach for Adaptive Sleep Modes in 5G Networks

Ali El Amine ^{1, 2} Mauricio Iturralde ³ Hussein Al Haj Hassan ⁴ Loutfi Nuaymi ^{1, 2}

1 IMT Atlantique - SRCD - Département Systèmes Réseaux, Cybersécurité et Droit du numérique

2 ADOPNET - Advanced technologies for operated networks

UR1 - Université de Rennes 1, IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire, IRISA_D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES

3 IRIT - Institut de recherche en informatique de Toulouse

4 Université Libanaise

Abstract : In 5G networks, specific requirements are defined on the periodicity of Synchronization Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) can be deactivated. On the other hand, BS densification is expected in 5G architecture. This will lead to an energy crunch if kept ignored. In this paper, we propose a distributed algorithm based on Reinforcement Learning (RL) that controls the states of the BSs while respecting the requirements of 5G. By considering different levels of Sleep Modes (SMs), the algorithm chooses how deep a BS can sleep according to the best switch-off SM level policy that maximizes the trade-off between energy savings and system delay. The latter is calculated based on the wake-up time required by the different SM levels. Results show that our algorithm performs better than the case of using only one type of SM. Furthermore, our simulations show a gain in energy savings up to 90% when the users are delay tolerant while respecting the periodicity of the SS bursts in 5G.

Keywords : Energy consumption 5G sleep modes Q-learning

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

Computer Science [cs] / Artificial Intelligence [cs.AI]

Engineering Sciences [physics] / Signal and Image processing

Engineering Sciences [physics] / Electric power

Liste complète des métadonnées

https://hal-imt-atlantique.archives-ouvertes.fr/hal-01988226
Contributor : Ali El Amine <>
Submitted on : Monday, January 21, 2019 - 4:02:53 PM
Last modification on : Friday, April 12, 2019 - 4:23:08 PM

A Distributed Q-Learning Appro...

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