Threshold-based power allocation algorithms for down-link switched-based parallel scheduling
Bibliographic record
Abstract
In this paper, we propose threshold-based power allocation algorithms for a recently proposed down-link switched based parallel scheduling (SBS) scheme and we present their performance results via computer simulations. As its name indicates it, the system re-allocates the extracted excess signal to noise ratio (SNR) from some acceptable users to unacceptable users among the scheduled users. After the power allocation process, the unacceptable users can reach acceptable SNRs and as such the number of effective acceptable users with an acceptable SNR threshold among the scheduled users is increased without any additional down-link transmit power. Some selected numerical results, show that the proposed power allocation algorithms offer a certain improvement in average spectral efficiency (ASE) and an increase in the average number of effective acceptable users. Although the average bit error rate (BER) performance degrades especially when the average SNR is close to the SNR threshold, this average BER performance still meets the average BER requirement.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".