Enhancing Network Slice Identification in Beyond 5G: A Comparative Study of Machine Learning Approaches
Bibliographic record
Abstract
Network Slice Identification (NSI) is crucial for managing Quality of Service (QoS) in Beyond 5G (B5G) networks, particularly for smart city applications. This study explores and compares supervised, unsupervised, and semi-supervised learning techniques for NSI, addressing the challenges of limited labelled data in production environments. We use a publicly available 5G network dataset to model and perform comparisons among supervised, unsupervised, and semi-supervised learning approaches. Our methodology involves feature selection, dimensionality reduction using t-SNE, and addressing class imbalance through undersampling. We evaluate model performance using accuracy and Silhouette Score. Our results show a Random Forest Classifier achieves 100% accuracy with supervised learning. The unsupervised K-Means clustering model, optimized with both t-SNE and undersampling, achieves a mean accuracy of 92.83%. Semi-supervised learning using a self-training method and being trained on only 10% of the training data points performs comparably to the supervised models. Importantly, we demonstrate the robustness check using test data perturbation injecting additional variability in data simulating unknown 5G network fluctuations. This comparative analysis provides insights into the trade-offs between different learning approaches for NSI in B5G networks, offering practical solutions for scenarios with different conditions of labelled data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".