Dynamic Fair Scheduling With QoS Constraints in Multimedia Wideband CDMA Cellular Networks
Bibliographic record
Abstract
A class of dynamic fair scheduling schemes based on the generalized processor sharing (GPS) fair service discipline, under the generic name code-division GPS (CDGPS), is proposed for a wideband direct-sequence code-division multiple-access (CDMA) cellular network to support multimedia traffic. The CDGPS scheduler makes use of both the traffic characteristics in the link layer and the adaptivity of the wideband CDMA physical layer to perform fair scheduling on a time-slot by time-slot basis, by using a dynamic rate-scheduling approach rather than the conventional time-scheduling approach. Soft uplink capacity is characterized for designing an efficient CDGPS resource allocation procedure. A credit-based CDGPS (C-CDGPS) scheme is proposed to further improve the utilization of the soft capacity by trading off the short-term fairness. Theoretical analysis shows that, with the C-CDGPS scheme, tight delay bounds can be provided to delay-sensitive traffic, and short-term unfairness can be bounded so that long-term weighted fairness for all users can still be satisfied. Simulation results show that bounded delays, increased throughput, and long-term fairness can be achieved for both homogeneous and heterogeneous traffic.
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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 | 0.002 |
| Open science | 0.002 | 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".