Proportional Fair Scheduling in Multi-Carrier Networks Using Channel Predictions
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Bibliographic record
Abstract
Proportional Fair Scheduling (PFS) is an important scheduling algorithm in wireless networks, aiming to utilize network resources efficiently while providing fairness among users. Recent progress in channel predictions makes it possible for the scheduler to take into account the achievable rates of users in the upcoming transmission intervals. In this paper we investigate the Predictive PFS in a multi-carrier system and show that the resulting optimization problem is a complex combinatorial problem in both time and frequency dimensions. We propose two suboptimal algorithms, and apply them to evaluate the effects of considering the predicted data rates on the performance of the network. Extensive simulations indicate improvements in both the achievable system throughput and access fairness among the users compared to the existing multichannel PFS method that does not use channel prediction.
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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