Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges
Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, software-driven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physica...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9695955/ |
| _version_ | 1818293979035205632 |
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| author | Bouziane Brik Karim Boutiba Adlen Ksentini |
| author_facet | Bouziane Brik Karim Boutiba Adlen Ksentini |
| author_sort | Bouziane Brik |
| collection | DOAJ |
| description | Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, software-driven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physical Network Function (PNF); (2) the notion of RAN controller that runs centrally RAN applications such as mobility management, users’ scheduling, radio resources allocation, etc. The RAN controller is in charge of enforcing the application decisions by using open interfaces with the RAN functions. One important feature introduced by O-RAN is the heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent RAN applications that are able to fulfill the Quality of Service (QoS) requirements of the envisioned 5G and beyond network services. In this work, we first give an overview of the evolution of RAN architectures toward 5G and beyond, namely C-RAN, vRAN, and O-RAN. We also compare them based on various perspectives, such as edge support, virtualization, control and management, energy consumption, and AI support. Then, we review existing DL-based solutions addressing the RAN part. We also show how they can be integrated/mapped to the O-RAN architecture since these works were not initially adapted to the O-RAN architecture. In addition, we present two case studies for DL techniques deployment in O-RAN. Furthermore, we describe how the main steps of deployed DL models in O-RAN can be automated, to ensure stable performance of these models, introducing ML system operations (MLOps) concept in O-RAN. Finally, we identify key technical challenges, open issues, and future research directions related to the Artificial Intelligence (AI)-enabled O-RAN architecture. |
| first_indexed | 2024-12-13T03:24:27Z |
| format | Article |
| id | doaj.art-331e801147384648af24baf2226aa217 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-12-13T03:24:27Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-331e801147384648af24baf2226aa2172022-12-22T00:01:17ZengIEEEIEEE Open Journal of the Communications Society2644-125X2022-01-01322825010.1109/OJCOMS.2022.31466189695955Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and ChallengesBouziane Brik0https://orcid.org/0000-0002-3267-5702Karim Boutiba1https://orcid.org/0000-0001-8883-7371Adlen Ksentini2https://orcid.org/0000-0002-0972-3091DRIVE EA1859, University of Bourgogne Franche-Comté, Besançon, FranceCommunication Systems Department, EURECOM, Sophia-Antipolis, FranceCommunication Systems Department, EURECOM, Sophia-Antipolis, FranceOpen Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, software-driven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physical Network Function (PNF); (2) the notion of RAN controller that runs centrally RAN applications such as mobility management, users’ scheduling, radio resources allocation, etc. The RAN controller is in charge of enforcing the application decisions by using open interfaces with the RAN functions. One important feature introduced by O-RAN is the heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent RAN applications that are able to fulfill the Quality of Service (QoS) requirements of the envisioned 5G and beyond network services. In this work, we first give an overview of the evolution of RAN architectures toward 5G and beyond, namely C-RAN, vRAN, and O-RAN. We also compare them based on various perspectives, such as edge support, virtualization, control and management, energy consumption, and AI support. Then, we review existing DL-based solutions addressing the RAN part. We also show how they can be integrated/mapped to the O-RAN architecture since these works were not initially adapted to the O-RAN architecture. In addition, we present two case studies for DL techniques deployment in O-RAN. Furthermore, we describe how the main steps of deployed DL models in O-RAN can be automated, to ensure stable performance of these models, introducing ML system operations (MLOps) concept in O-RAN. Finally, we identify key technical challenges, open issues, and future research directions related to the Artificial Intelligence (AI)-enabled O-RAN architecture.https://ieeexplore.ieee.org/document/9695955/B5G networksRANopen RAN architectureRAN intelligent controllerdeep learningMLOps |
| spellingShingle | Bouziane Brik Karim Boutiba Adlen Ksentini Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges IEEE Open Journal of the Communications Society B5G networks RAN open RAN architecture RAN intelligent controller deep learning MLOps |
| title | Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges |
| title_full | Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges |
| title_fullStr | Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges |
| title_full_unstemmed | Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges |
| title_short | Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges |
| title_sort | deep learning for b5g open radio access network evolution survey case studies and challenges |
| topic | B5G networks RAN open RAN architecture RAN intelligent controller deep learning MLOps |
| url | https://ieeexplore.ieee.org/document/9695955/ |
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