Queue-Aware Channel-Adapted Scheduling and Congestion Control for Best-Effort Services in LTE Networks
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it