A Network Architecture for Scalable End-to-End Management of Reusable AI-Based Applications in 6G Networks
DOI:
https://doi.org/10.32628/CSEIT251112104Keywords:
6G Networks, Artificial Intelligence, Network Architecture, Distributed Learning, Network ManagementAbstract
This article presents a comprehensive network architecture for managing reusable AI-based applications in 6G networks, addressing the critical challenge of AI silos in current implementations. It introduces a unified approach to data collection, feature extraction, model management, and application integration across network domains. By implementing standardized workflows and shared resources, the architecture enables efficient end-to-end management while promoting reusability and scalability. The solution incorporates a unified data collection layer, shared feature repository, model management framework, and application integration layer, all designed to support the demanding requirements of next-generation networks. Through multiple use cases including RAN optimization, network security, and service quality management, the article demonstrate the architecture's effectiveness in real-world scenarios. The results show significant improvements in development efficiency, resource utilization, scalability, and maintenance operations. It contributes to the evolution of 6G networks by providing a structured approach to integrating AI capabilities while preventing the formation of isolated solutions.
Downloads
References
Muntadher Alsabah et al., "6G Wireless Communications Networks: A Comprehensive Survey," IEEE Access, Nov. 2021. Available: https://ieeexplore.ieee.org/document/9598915
Salim El khediri et al., "Integration of artificial intelligence (AI) with sensor networks: Trends, challenges, and future directions," Journal of King Saud University - Computer and Information Sciences Volume 36, Issue 1, January 2024. Available: https://www.sciencedirect.com/science/article/pii/S1319157823004469
Sree Krishna Das et al., "Distributed Learning for 6G–IoT Networks: A Comprehensive Survey," Research Gate Publication, July 2022. Available: https://www.researchgate.net/publication/376379049_Distributed_Learning_for_6G-IoT_Networks_A_Comprehensive_Survey
Zhengquan Zhang et al., "6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies," IEEE Vehicular Technology Magazine, Volume 14, Issue 3, 2019. Available: https://ieeexplore.ieee.org/document/8766143
Swati Lakshmi Boppana et al., "A Machine Learning Approach in Communication 5G-6G Network," Journal of Theoretical and Applied Information Technology, vol. 102, no. 10, May 2024. Available: https://www.jatit.org/volumes/Vol102No10/6Vol102No10.pdf
Merima Kulin et al., "A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer," Electronics, vol. 10, no. 3, 2021. Available: https://www.mdpi.com/2079-9292/10/3/318
Khaled B. Letaief et al., "The Roadmap to 6G: AI Empowered Wireless Networks," IEEE Communications Magazine, Volume 57, Issue 8, August 2019. Available: https://ieeexplore.ieee.org/document/8808168
Nasir Abbas et al., "Mobile Edge Computing: A Survey," IEEE Internet of Things Journal, Volume 5, Issue 1, Sep. 2017. Available: https://ieeexplore.ieee.org/document/8030322
A. Clemm and O. Festor, "Network Management: Current Trends and Future Perspectives," Journal of Network and Systems Management 14(4):483-491, Dec. 2006. Available: https://www.researchgate.net/publication/220575967_Network_Management_Current_Trends_and_Future_Perspectives
Jihong Park et al., "Wireless Network Intelligence at the Edge," arXiv preprint arXiv:1812.02858, 2018. Available: https://arxiv.org/abs/1812.02858
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.