Bibliographic record
Abstract
The stability regions of two opportunistic scheduling policies, i.e., utility-based (UB) scheduling and the channel-rate-based (CRB) scheduling, in wireless networks are discussed, respectively. UB scheduling is a generalized proportional fair scheduling in an unsaturated system, and CRB scheduling is a variant of the UB scheduling. We give the closed-form expression of the stability region of CRB scheduling and a numerical method to obtain the stability region of UB scheduling. Both two scheduling policies are not throughput optimal, and thus, in general, their stability regions are less than the ergodic capacity region. With CRB scheduling, the stability region is a convex hull, whereas with UB scheduling, the stability region is generally even nonconvex and may exhibit some undesirable properties such as decreasing the traffic of one flow leading another flow being unstable and proportionally decreasing the traffic of all flows leading a stable system to be unstable. We further show that, as long as the arrival rate lies inside the ergodic capacity region, we can assign a proper weight to each user, and based on the weighted UB/CRB scheduling policies, the system can be stabilized. Detailed numerical examples and simulations are given to illustrate the stability region of the two policies and validate our analysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".