AQuA: aggregated queueing algorithm for CDMA2000 base station controll
Bibliographic record
Abstract
The increasing need for enhanced data services in cellular networks requires sharing of scarce wireless channel resources among the mobile users by dynamic channel assignments. It has been shown in previous works that such a scheme causes queue management problems in the buffers shared by multiple mobiles, e.g. the input buffer at base station controller (BSC), when their rates are increased after a period of lower aggregate data rate in radio links. Traditionally buffer management techniques like random early detection (RED) are used for a single buffer only. In this paper, we extend the RED algorithm to an aggregated queueing algorithm (AQuA) that simultaneously regulates the queueing discipline in both the shared and individual link buffers so that buffer overflow problems after aggregate link rate increase do not occur. Our algorithm relies on standard information on queueing backlog in link buffers available at BSC to perform buffer management in a unified manner with shared buffer. Furthermore, we demonstrate that our approach provides significantly greater determinism in packet transit delays over BSC and greater throughputs and similar levels of fairness as RED mechanism in shared buffers in conjunction with unregulated link buffers
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".