Online Convex Optimization for Dynamic RAN Slicing with Quality of Service
Bibliographic record
Abstract
With the emergence of 5G and beyond networks, simultaneous resource allocation to a diverse range of services and applications across various industries has been a trending topic. Achieving efficient resource allocation for different services and applications requires a unified abstraction of available resources, which is commonly known as Network Slicing. However, allocating resources among slices becomes a non-trivial problem due to the limited resources and unpredictable requirements of different services. In this paper, we study a dynamic RAN slicing framework incorporating multiple base stations, where workload distribution, radio spectrum and computing resource allocation decisions are made to meet diverse Quality of Service (QoS) requirements. Specifically, delay-tolerant and delay-sensitive services are considered. The unit resource allocation costs are considered to be time-varying. Furthermore, the unit resource allocation costs and QoS requirements are considered to be unknown before making workload distribution and resource allocation decisions. We propose an Online Convex Optimization (OCO) approach for the RAN slicing framework in order to minimize the overall cost and satisfy the QoS constraints in the long run. Simulation results and comparison with two baselines demonstrate that our algorithm outperforms the baselines while satisfying QoS constraints in long-term.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".