AI Methods in Network Slice Life-Cycle Phases: A Survey
Network slicing (NS) plays a vital role in enabling flexible and efficient resource allocation, tailored to diverse use cases and network domains. This survey paper explores the synergy between NS and Artificial Intelligence (AI), emphasizing how Machine Learning (ML) techniques can address challenges across the slice life-cycle. A key contribution of this work is an in-depth analysis of AI and primarily ML applications in each phase of the slice life-cycle, delving into their specific tasks and discussing the techniques applied to these tasks. Furthermore, we present a taxonomy based on different slicing criteria, offering a structured perspective to enhance understanding and implementation.
Authors
- Aggeliki Sgora (ORCID: https://orcid.org/0000-0001-8265-6692)
- Evangelos Thomatos
- Periklis Chatzimisios (ORCID: https://orcid.org/0000-0003-2366-1365)
- Athanasios Tsipis (ORCID: https://orcid.org/0000-0001-5069-3376)
Institutions
- University of New Mexico (US)
- International Hellenic University (GR)
- Ionian University (GR)
Publication Details
- Journal
- Electronics
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3390/electronics14204053
- Primary Topic
- Software-Defined Networks and 5G
- Type
- article
- Field-Weighted Citation Impact
- 0.00