Dynamic Scheduling with Statistical Delay Guarantees and Traffic Dropping
Why this work is in the frame
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Bibliographic record
Abstract
This work studies the dynamic scheduling problems in wireless networks with delay-sensitive loss-tolerant users. The users' traffic satisfies some statistical delay constraints. Moreover, the traffic can be dropped but the dropping rates do not exceed some thresholds. We consider two scheduling scenarios. First, we study the problem to minimize the total transmission power while maintaining the minimum rates for the users. Then, we study the problem to maximize the minimum rate(s) of the users while constraining the maximum total power. We derive the optimal solutions for both scheduling problems. When the fading statistics are available, using the dual-gradient method, the optimal policies can be computed. When the fading statistics are unknown, this work proposes online scheduling algorithms using online time-averaging. The convergence and optimality of the proposed algorithm are guaranteed by the results in stochastic approximation theory.
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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