Opportunistic Dual Metric Scheduling Algorithm for LTE uplink
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".