Opportunistic Dual Metric Scheduling Algorithm for LTE uplink
Why this work is in the frame
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Bibliographic record
Abstract
LTE uplink has two major scheduling algorithms, namely, Best CQI (BCQI) algorithm and Proportional Fairness (PF) algorithm. PF algorithm provides less system throughput than BCQI algorithm, however, unlike BCQI algorithm it considers users with poor channel condition for allocation process. In this paper, a new scheduling algorithm called as Opportunistic Dual Metric (ODM) Scheduling Algorithm is proposed for LTE uplink. The objective of the algorithm is to prioritize the users with good channel condition for resource allocation, at the same time not to starve the users with poor channel conditions. The proposed algorithm has two resource allocation matrices which are effectively used to allocate the resources to the users. The performance of ODM is measured in terms of throughput, fairness and transmit power. From the results it is observed that the proposed algorithm has better trade-off in terms of all the three performance parameters than PF scheduler and BCQI scheduler.
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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