A Multi-Armed Bandit Game for Multi-Tenant RAN Slicing
Bibliographic record
Abstract
Network slicing is an auspicious technology in 5G networks that can handle different scenarios and use cases. Resource scheduling is essential for improving resource-multiplexing gain among slices while meeting specific service requirements for Radio Access Network (RAN) slicing. It needs to be reconfigured adaptively to reduce re-slicing frequency, maximize network resource utilization, and guarantee performance and isolation between multi-tenants under dynamic traffic load. However, the resource reconfiguration problem is intractable due to the high computational complexity caused by the numerous variables. Thus, this paper proposes an intelligent resource reconfiguration for multi-tenant RAN slicing. We explore a collaborative online learning framework consisting of multi-armed bandits (MAB) for tackling small-time-scale resource allocation and propose the Exp3 algorithm. Based on the historical traffic data, our approach assists the RAN in making online resource re-scheduling decisions. Extensive simulations validate our proposed Learning-Based approach’s effectiveness compared with other benchmark algorithms (e.g., Threshold-Based, Periodic slicing). In addition, we investigate the Exp3 algorithm under different parameter configurations. Numerical results show the convergence of our proposed model, improve operator satisfaction and resource utilization, and reduce the frequency of re-slicing by ensuring multi-service and multi-tenant isolation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".