Collaborative cross system AI : toward 5G system and beyond
|Author:||Bagaa, Miloud1; Taleb, Tarik1,2,3; Riekki, Jukka2;|
1Department of Communications and Networking, School of Electrical Engineering, Aalto University, Finland
2Faculty of Information Technology and Electrical Engineering, Oulu University
3Department of Computer and Information Security, Sejong University, Seoul 05006, South Korea
4Department of Computer and Information Security, Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, South Korea
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021102752477
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-10-27
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.
|Pages:||286 - 294|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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 CHAR-ITY). 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.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
311654 (Academy of Finland Funding decision)
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