Mobile Virtual Network Admission Control and Resource Allocation for Wireless Network Virtualization: A Robust Optimization Approach
Bibliographic record
Abstract
Wireless network virtualization is a promising technology in next generation wireless networks. In this paper, motivated by the experience of user equipment (UE) admission control in traditional wireless networks, we propose a novel concept of mobile virtual network (MVN) admission control for wireless virtualization. By limiting the number of MVNs embedded in the physical network, MVN admission control can effectively guarantee quality of service (QoS) experienced by users of MVNs and maximize the utilization of the physical networks at the same time. Specifically, we propose a two-stage MVN embedding mechanism that can decouple short-term physical resource allocation from long-term admission control and resource leasing. With recent advances in robust optimization, we formulate the MVN admission control problem as a robust optimization problem. Both the long-term admission control and short- term resource allocation problems are transformed to convex problems, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".