TY - JOUR
T1 - AI/ML for beyond 5G systems: Concepts, technology enablers & solutions
AU - Taleb, Tarik
AU - Benzaïd, Chafika
AU - Addad, Rami Akrem
AU - Samdanis, Konstantinos
N1 - Funding Information:
This research work is partially supported by the European Union’s Horizon 2020 Research and Innovation Program through the aerOS project under Grant No. 101069732 ; the Business Finland 6Bridge 6Core project under Grant No. 8410/31/2022 ; the Research Council of Finland (former Academy of Finland) under IDEA-MILL project (Grant No. 352428 ) and 6G Flagship program (Grant No. 346208 ); and the European Union’s HE research and innovation programe HORIZON-JU-SNS-2022 under the RIGOUROUS project (Grant No. 101095933 ).
Funding Information:
This research work is partially supported by the European Union's Horizon 2020 Research and Innovation Program through the aerOS project under Grant No. 101069732; the Business Finland 6Bridge 6Core project under Grant No. 8410/31/2022; the Research Council of Finland (former Academy of Finland) under IDEA-MILL project (Grant No. 352428) and 6G Flagship program (Grant No. 346208); and the European Union's HE research and innovation programe HORIZON-JU-SNS-2022 under the RIGOUROUS project (Grant No. 101095933).
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/12
Y1 - 2023/12
N2 - 5G brought an evolution on the network architecture employing the service-based paradigm, enabling flexibility in realizing customized services across different technology domains. Such paradigm gives rise to the adoption of analytics and Artificial Intelligence/Machine Learning (AI/ML) in mobile communications with the ease of collecting various measurements related to end-users and the network, which can be exposed towards consumers, including 3rd party applications. AI/ML may influence network planning and optimization considering the service life-cycle and introduce new operations provision, paving the way towards 6G. This article provides a survey on AI/ML considering the business, the fundamentals and algorithms across the radio, control, and management planes. It sheds light on the key technologies that assist the adoption of AI/ML in 3rd Generation Partnership Project (3GPP) networks considering service request, reporting, data collection and distribution and it overviews the main AI/ML algorithms characterizing them into user-centric and network-centric. Finally, it explores the main standardization and open source activities on AI/ML, highlighting the lessons learned and the further challenges that still need to be addressed to reap the benefits of AI/ML in automation for beyond 5G/6G mobile systems.
AB - 5G brought an evolution on the network architecture employing the service-based paradigm, enabling flexibility in realizing customized services across different technology domains. Such paradigm gives rise to the adoption of analytics and Artificial Intelligence/Machine Learning (AI/ML) in mobile communications with the ease of collecting various measurements related to end-users and the network, which can be exposed towards consumers, including 3rd party applications. AI/ML may influence network planning and optimization considering the service life-cycle and introduce new operations provision, paving the way towards 6G. This article provides a survey on AI/ML considering the business, the fundamentals and algorithms across the radio, control, and management planes. It sheds light on the key technologies that assist the adoption of AI/ML in 3rd Generation Partnership Project (3GPP) networks considering service request, reporting, data collection and distribution and it overviews the main AI/ML algorithms characterizing them into user-centric and network-centric. Finally, it explores the main standardization and open source activities on AI/ML, highlighting the lessons learned and the further challenges that still need to be addressed to reap the benefits of AI/ML in automation for beyond 5G/6G mobile systems.
KW - AI/ML
KW - Data analytics
KW - Intelligent networks
KW - Network automation
UR - http://www.scopus.com/inward/record.url?scp=85173036303&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2023.110044
DO - 10.1016/j.comnet.2023.110044
M3 - Article
AN - SCOPUS:85173036303
SN - 1389-1286
VL - 237
JO - Computer Networks
JF - Computer Networks
M1 - 110044
ER -