5G Network Slice Type Classification using Traditional and Incremental Learning
Bibliographic record
Abstract
The Fifth generation (5G) mobile network is expected to provide high bandwidth, low latency, and rapid user connectivity. 5G Mobile operators are seeking an effective solution that would enable them to support heterogeneous use cases with different Quality of Service (QoS) requirements by utilizing the existing physical infrastructure. 5G supports Network Slicing (NS), an end-to-end (E2E) logical network that is mutually isolated, has independent control, and can be managed independently. By slicing the network, mobile operators can effectively manage several network instances over a single infrastructure to provide a variety of applications, use cases, and business services while satisfying heterogeneous QoS requirements. With the advancement of Machine Learning (ML), future communication networks will need to use data-driven decision-making to achieve desired network performance. In this paper, we demonstrated a prediction mechanism using Machine and Deep Learning Algorithms in traditional and incremental ways to select the suitable network slice for various user requirements and device types. Using a publicly available dataset and Incremental Learning model called Stochastic Gradient Descent (SGD), we successfully classified incoming user requests to appropriate network slices with an accuracy of 99.33%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".