Machine Learning in Network Slicing - A Survey - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Access Année : 2023

Machine Learning in Network Slicing - A Survey

Résumé

5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.
Fichier principal
Vignette du fichier
Machine_Learning_in_Network_Slicing___A_Survey_Clean.pdf (4.07 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04189558 , version 1 (28-08-2023)

Licence

Paternité

Identifiants

Citer

Hnin Pann Phyu, Diala Naboulsi, Razvan Stanica. Machine Learning in Network Slicing - A Survey. IEEE Access, 2023, 11, pp.39123-39153. ⟨10.1109/ACCESS.2023.3267985⟩. ⟨hal-04189558⟩
21 Consultations
137 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More