HOL delay based scheduling in wireless networks with flow-level dynamics
Why this work is in the frame
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Bibliographic record
Abstract
How to design a throughput-optimal scheduling algorithm in a heterogeneous wireless network with flow-level dynamics is a challenging problem. In this paper, we investigate the properties of a Head-of-Line (HOL) delay based scheduling algorithm, and prove that it can achieve throughput-optimality with flow-level dynamics. The algorithm is easy to implement because it requires no prior knowledge of the statistics of the arrival traffic and channel state information. Extensive simulations have been conducted to validate the theoretical conclusion and evaluate the performance. It is shown that, at the presence of flow-level dynamics, the HOL delay based scheduling algorithm can outperform the classic queue-length based MaxWeight scheduling, and can achieve a similar performance as other known throughput-optimal scheduling while it is simpler and more practical to implement.
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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