Resource Allocation in HSDPA Using Best-Users Selection Under Code Constraints
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Bibliographic record
Abstract
We consider the open issue of resource allocation in HSDPA networks for the purpose of enhancing the system's performance both in terms of throughput and fairness while taking into consideration resource constraints specific to the HSDPA architecture. In particular, we propose a two-best user scheduling approach with an optimal power allocation aimed at maximizing data throughput and a selection criterion designed to ensure adaptive proportional fairness between users with different resource requirements and constraints. Compared to the popular carrier-to-interference ratio (CIR) and proportional fairness (PF) methods, the proposed technique, called two-best adaptive proportional fairness (APF) is shown to provide higher performance both in terms of throughput and fairness even when users experience different channel propagating conditions.
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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