On the Delay-Fairness through Scheduling for Wireless OFDMA Networks
Bibliographic record
Abstract
This paper studies QoS-guaranteed fair resource allocation and packet scheduling for OFDMA networks. We start with non-traffic-aware weighted generalized proportional fair (WGPF) scheduler and make it a delay-fair scheduler. Delay-fair objective is to equalize delay dissatisfaction measures among users. In this paper the focus is on mean queuing delay. We show how the WGPF objectives are connected and are equivalent to delay-fair scheduler, derived from Little's law. We see that our fair framework can also be interpreted as minimizing the total delay dissatisfaction that users are experiencing. This framework extends the conventional fairness notions in order to handle heterogeneity of traffic in time and among users which is an important emerging problem. We then study an important special case, which is min-max delay fairness. We prove that the developed framework for a special dissatisfaction function, asymptotically leads to the min-max average delays. The developed framework, in this special case, can be adjusted between two extreme objectives: minimizing total average delay among users and minimizing the maximum average delay among users. Finally we prove that the gradient scheduling algorithm is equivalent asymptotically to serving the user with the largest average delay at each iteration.
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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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".