Zero-Touch Slicing: Revolutionizing 5G Network Management through AI and Automation
DOI:
https://doi.org/10.32628/CSEIT2410612437Keywords:
Network Slicing, Artificial Intelligence, Network Automation, Machine Learning, 5G NetworksAbstract
Zero-Touch Slicing represents a revolutionary approach in modern telecommunications networks, particularly in 5G and future 6G systems, leveraging artificial intelligence and machine learning for automated network management. This comprehensive article explores the fundamental architecture, implementation strategies, and real-world applications of Zero-Touch Slicing. The article examines how AI-driven automation transforms traditional network management through predictive analytics, automated decision-making, and closed-loop optimization. It evaluates the technology's impact across enterprise environments, smart city infrastructures, and dynamic service provisioning scenarios. The article demonstrates significant improvements in operational efficiency, service quality, and business agility through automated network slice management. Furthermore, it analyzes the future implications and benefits of this technology, highlighting its potential to revolutionize network operations in the evolving telecommunications landscape.
📊 Article Downloads
References
GSMA Intelligence, "The Mobile Economy 2024," GSMA, Tech. Rep. 2024. [Online]. Available: https://www.gsma.com/solutions-and-impact/connectivity-for-good/mobile-economy/wp-content/uploads/2024/02/260224-The-Mobile-Economy-2024.pdf
Shalli Rani, et al., "Network Slicing for Zero-Touch Networks: A Top-Notch Technology," IEEE 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10274564 DOI: https://doi.org/10.1109/MNET.2023.3320394
Estefanía Coronado, et al., "Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks," IEEE 2022. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9913206 DOI: https://doi.org/10.1109/COMST.2022.3212586
Suvidha Mhatre, et al., "Intelligent QoS-aware Slice Resource Allocation with User Association Parameterization for Beyond 5G O-RAN-based Architecture Using DRL," IEEE 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10737377 DOI: https://doi.org/10.1109/TVT.2024.3483288
Md. Ariful Islam Arif, et al., "Machine Learning and Deep Learning Based Network Slicing Models for 5G Network," Computer Communications, 2023. [Online]. Available: https://www.researchgate.net/publication/369000128_Machine_Learning_and_Deep_Learning_Based_Network_Slicing_Models_for_5G_Network DOI: https://doi.org/10.1109/ICCIT57492.2022.10054696
Kairi Tokuda, et al., "Network slice reconfiguration with deep reinforcement learning under variable number of service function chains," Computer Networks, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1389128623000816 DOI: https://doi.org/10.1016/j.comnet.2023.109636
Kavya Govindarajan, et al., "Closed loop optimization of 5G network slices," ACM Digital Library, 2022. [Online]. Available: https://dl.acm.org/doi/10.1145/3564695.3564776 DOI: https://doi.org/10.1145/3564695.3564776
Min Xie, et al., "AI-Driven Closed-Loop Service Assurance with Service Exposures" in EuCNC 2020: 2020. [Online]. Available: https://web-backend.simula.no/sites/default/files/publications/files/eucnc2020.pdf DOI: https://doi.org/10.1109/EuCNC48522.2020.9200943
Madhusanka Liyanage, et al., "A survey on Zero touch network and Service Management (ZSM) for 5G and beyond networks," Journal of Network and Computer Applications, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1084804522000297 DOI: https://doi.org/10.1016/j.jnca.2022.103362
Mirna El Rajab, et al., "Zero-touch networks: Towards next-generation network automation" Computer Networks, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1389128624001269 DOI: https://doi.org/10.1016/j.comnet.2024.110294
Wen Wu, et al., "AI-Native Network Slicing for 6G Networks," Computer Networks Journal, 2022. [Online]. Available: https://www.researchgate.net/publication/359723970_AI-Native_Network_Slicing_for_6G_Networks [12] H. B. Mahesh et al., "The Network Slicing and Performance Analysis of 6G Networks using Machine Learning" EMITTER International Journal of Engineering Technology 2024. [Online]. Available: https://www.researchgate.net/publication/377446387_The_Network_Slicing_and_Performance_Analysis_of_6G_Networks_using_Machine_Learning
Downloads
Published
Issue
Section
License
Copyright (c) 2024 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.
