Queue-Aware Channel-Adapted Scheduling and Congestion Control for Best-Effort Services in LTE Networks
Bibliographic record
Abstract
In this paper, we study the performance of long-term evolution (LTE) for various types of channel-adapted scheduling for nonreal-time flows, while an end-to-end congestion control algorithm controls the rate of elastic traffic at the end users. First, we propose a new type of queue-aware channel-adapted scheduling at a base station, and explain how it allocates resources to competing nonreal-time flows where channel conditions are time-varying. We also introduce a new congestion measure function for a minimum cost flow control (MCFC) algorithm in the LTE and call it an individual flow-based congestion measure. We show that using different combinations of channel-adapted scheduling at the base station and congestion control algorithms can lead to major differences in the obtained throughput and fairness for the best-effort traffic. The results clearly show that the transport protocol and scheduling algorithm can cause significant conflict in some situations. We show the advantages of the proposed queue-aware channel-adapted scheduling in performance improvement and we also show that the combination of an MCFC algorithm (in which the new individual flow-based congestion measure is applied), with queue-aware proportional fair scheduling, leads to a better tradeoff between overall throughput and fairness compared with the other studied combinations.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".