Redefining 6G Network Slicing: AI-Driven Solutions for Future Use Cases
Open accessAbstract
The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks.
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The 5G/6G transition compounds IoT security by introducing network slicing whose isolation guarantees become targets of slice-hopping attacks.
“The 5G/6G transition compounds the problem by introducing network slicing-logical partitions whose isolation guarantees become themselves the target of slice-hopping attacks”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [48]
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- Authors
- Botez, R., Zinca, D., & Dobrota, V.
- Affiliations
- Technical University of Cluj-Napoca (RO)
- Communications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2025-01-18
- Volume
- 14
- Issue
- 2
- Pages
- 368
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4406617796
- Cited by
- 29
- References
- 18
- Citations by year
- 2026: 13 · 2025: 16
- FWCI
- 12.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- synthetic dataset, slice selection, handover prediction, ultra-reliable low-latency communication, machine-type communication
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