An Adaptive Non-preemptive Scheduling Framework for Delay Bounded Traffic in Cellular Networks
Why this work is in the frame
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Bibliographic record
Abstract
Provisioning multimedia streaming services to mobile users in next generation wireless networks is considered critical to the successful deployment of such networks. Streaming traffic is characterized by the need of relatively high data transmission rates, and the need to limit the wireless network delays during transmission. Such factors contribute to the importance of the design and use of scheduling mechanisms that work at the streaming connection level to manage network resources. In this paper, we consider the problem of designing schedulers that aim at maximizing the achieved throughput subject to constraints on the maximum acceptable delay that can be tolerated by each traffic stream. We propose an adaptive scheduling framework for the non-preemptive delivery of traffic streams in cellular networks where a fixed number of channels are allocated to streaming services. The obtained simulation results indicate the competitiveness of the proposed design when used online to control traffic, and the usefulness of the underlying algorithms when used offline to analyze traffic traces
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it