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A Survey on Green Enablers: A Study on the Energy Efficiency of AI-Based 5G Networks.
Ezzeddine, Zeinab; Khalil, Ayman; Zeddini, Besma; Ouslimani, Habiba Hafdallah.
Afiliação
  • Ezzeddine Z; Laboratory of Electrochemistry of Materials for Energetics (LEME) EA 4416, University Paris, 92410 Nanterre, France.
  • Khalil A; SATIE Laboratory CNRS-UMR 8029, CY Tech, CY Cergy Paris University, 95000 Cergy, France.
  • Zeddini B; Adnan Kassar School of Business, Lebanese American University, Beirut 1102-2801, Lebanon.
  • Ouslimani HH; SATIE Laboratory CNRS-UMR 8029, CY Tech, CY Cergy Paris University, 95000 Cergy, France.
Sensors (Basel) ; 24(14)2024 Jul 16.
Article em En | MEDLINE | ID: mdl-39066007
ABSTRACT
In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI's rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI's integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article