Reseach Article
Network Slice Recognition With Explainable Machine Learning
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 7 |
| Year of Publication: 2025 |
| Authors: Debmalya Ray |
10.5120/jaai202436
|
Debmalya Ray . Network Slice Recognition With Explainable Machine Learning. Journal of Advanced Artificial Intelligence. 1, 7 ( Jun 2025), 9-14. DOI=10.5120/jaai202436
Abstract
Fifth-generation (5G) and beyond networks various emerging applications such as AR/VR/XR, e-Health, live video streaming, and automated vehicles are expected to have diverse and strict quality of service (QoS) requirements. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. It enables the creation of multiple virtual networks, each operating on a shared physical infrastructure, to meet various application requirements. This approach allows for customized network environments tailored to specific needs, such as different Quality of Service (QoS) levels, security protocols, and performance characteristics. This paper also envisages the usage of Explainable AI which plays a significant role in making machine learning models more transparent and understandable. In the context of network slicing and other telecom applications which enhance the interpretability and trustworthiness of machine learning models. This is essential for effective decision making and maintaining high service standards.
References
- J. Ordonez-Lucena, P. Ameigeiras, D. Lopez, J. J. Ramos-Munoz, J. Lorca and J. Folgueira, "Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges," in IEEE Communications Magazine, vol. 55, no. 5, pp. 80-87, May 2017, doi: 10.1109/MCOM.2017.1600935
- Zhang, Q.; Liu, F.; Zeng, C. Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice. IEEE/ACM Trans. Netw. 2021, 29, 2115–2128. [CrossRef]
- Zhang, H.; Liu, N.; Chu, X.; Long, K.; Aghvami, A.; Leung, V. Network Slicing Based 5G and Future Mobile Networks: Mobility. In Resource Management, and Challenges; IEEE Communications Magazine: 2017. Available online: https://ieeexplore.ieee.org/ abstract/document/8004168 (accessed on 17 March 2023).
- Barakabitze, A.A.; Ahmad, A.; Mijumbi, R.; Hines, A. 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Comput. Netw. 2020, 167, 106984. [CrossRef]
- Babbar, H.; Rani, S.; AlZubi, A.A.; Singh, A.; Nasser, N.; Ali, A. Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions. IEEE Wirel. Commun. 2022, 29, 112–118. [CrossRef]
- A. Thantharate, R. Paropkari, V. Walunj and C. Beard, "DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks," 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2019, pp. 0762-0767, doi: 10.1109/UEMCON47517.2019.8993066
- Hurtado Sánchez, Johanna Andrea, Katherine Casilimas, and Oscar Mauricio Caicedo Rendon. 2022. "Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey" Sensors 22, no. 8: 3031. https://doi.org/10.3390/s22083031
- Domeke, Afra, Bruno Cimoli, and Idelfonso Tafur Monroy. 2022. "Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems" Applied Sciences 12, no. 13: 6617. https://doi.org/10.3390/app12136617
- Hurtado Sánchez, Johanna Andrea, Katherine Casilimas, and Oscar Mauricio Caicedo Rendon. 2022. "Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey" Sensors 22, no. 8: 3031. https://doi.org/10.3390/s22083031
- Venkatapathy, Sujitha, Thiruvenkadam Srinivasan, Han-Gue Jo, and In-Ho Ra. 2023. "An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning" Sensors 23, no. 23: 9608. https://doi.org/10.3390/s23239608
- Mlika, Z., Cherkaoui, S. Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach. Ann. Telecommun. 76, 665–683 (2021). https://doi.org/10.1007/s12243-021-00872-w
- Abood, M. S., Wang, H., He, D., Kang, Z., & Kawoya, A. (2023). Intelligent Network Slicing in V2X Networks – A Comprehensive Review. Journal of Artificial Intelligence and Technology, 3(2), 75–84. https://doi.org/10.37965/jait.2023.0208
- Zahoor, Sumbal, Ishtiaq Ahmad, Mohamed Tahar Ben Othman, Ali Mamoon, Ateeq Ur Rehman, Muhammad Shafiq, and Habib Hamam. 2022. "Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges" Sensors 22, no. 17: 6623. https://doi.org/10.3390/s22176623
- Thantharate, Anurag, Ankita Vijay Tondwalkar, Cory Beard, and Andres Kwasinski. 2022. "ECO6G: Energy and Cost Analysis for Network Slicing Deployment in Beyond 5G Networks" Sensors 22, no. 22: 8614. https://doi.org/10.3390/s22228614
- Rakkiannan, T., Ekambaram, G., Palanisamy, N. et al. An Automated Network Slicing at Edge with Software Defined Networking and Network Function Virtualization: A Federated Learning Approach. Wireless Pers Commun 131, 639–658 (2023). https://doi.org/10.1007/s11277-023-10450-z.
- Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, Adeel Rafiq, and Wang-Cheol Song. 2020. "Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks" Electronics 9,no.10:1710.https://doi.org/10.3390/electronics9101710
Index Terms
Keywords