Reinforcement learning approach for Advanced Sleep Modes management in 5G networks
Autor: | Azeddine Gati, Eitan Altman, Tijani Chahed, Fatma Ezzahra Salem, Zwi Altman |
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Přispěvatelé: | Département Réseaux et Services de Télécommunications (RST), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Orange Labs [Chatillon], Orange Labs, Centre National de la Recherche Scientifique (CNRS), Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Network Engineering and Operations (NEO ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Réseaux et Services de Télécommunications (TSP - RST) |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Computer science
05 social sciences 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Energy consumption Constraint (information theory) Reduction (complexity) Base station [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 0508 media and communications Control theory 0202 electrical engineering electronic engineering information engineering Reinforcement learning Energy (signal processing) 5G |
Zdroj: | Proceedings VTC-FALL 2018 : 88th Vehicular Technology Conference VTC-FALL 2018 : 88th Vehicular Technology Conference VTC-FALL 2018 : 88th Vehicular Technology Conference, Aug 2018, Chicago, United States. pp.1-5, ⟨10.1109/VTCFall.2018.8690555⟩ VTC-Fall |
Popis: | International audience; Advanced Sleep Modes (ASMs) correspond to a gradual deactivation of the Base Station (BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep Modes (SMs) can be considered according to the transition time (deactivation and activation durations) of each component. We propose in this paper a management solution for ASMs based on Q-learning approach. The target is to find the optimal durations for each SM level according to the requirements of the network operator in terms of EC reduction and delay constraints. The proposed solution shows that even with a high constraint on the delay, we can achieve high energy savings (almost 57% of EC reduction) without inducing any impact on the delay. When the delay constraint is relaxed, we can achieve up to almost 90% of energy savings |
Databáze: | OpenAIRE |
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