Collaborative Cross System AI

Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Degree programme
IEEE NETWORK, Volume 35, issue 4
The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging and conflicting requirements of the verticals make the orchestration and management of networks complicated and challenging. Recent advances in network automation and artificial intelligence (AI) create enthusiasm from industries and academia toward applying these concepts and techniques to tackle these challenges. With these techniques, the network can be autonomously optimized and configured. This article suggests a collaborative cross-system AI that leverages diverse data from different segments involved in the end-to-end communication of a service, diverse AI techniques, and diverse network automation tools to create a self-optimized and self-orchestrated network that can adapt according to the network state. We align the proposed framework with the ongoing network standardization.
Funding Information: AcknoWledgment The research leading to these results received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101016509 (project CHARITY). The article reflects only the authors’ views. The Commission is not responsible for any use that may be made of the information it contains. The research work is also partially funded by the Academy of Finland 6Genesis project under Grant No. 318927, and by the Academy of Finland CSN project under Grant No. 311654. Prof. Song was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) (No. 2020-0-00959). Dr. Miloud Bagaa work was supported by the CSN project. Publisher Copyright: © 1986-2012 IEEE.
Other note
Bagaa , M , Taleb , T , Riekki , J & Song , J S 2021 , ' Collaborative Cross System AI : Toward 5G System and beyond ' , IEEE NETWORK , vol. 35 , no. 4 , 9409842 , pp. 286-294 .