Analysis of Practical Frequency Selective Scheduling Algorithms in LTE Networks
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Bibliographic record
Abstract
The use of orthogonal frequency division multiple access (OFDMA) in Long Term Evolution (LTE) and LTE- Advanced systems facilitates the potential for scheduling cell users on orthogonal time-frequency resource blocks selectively. This paper identifies several frequency selective scheduling (FSS) algorithms and studies their performance and optimality under certain identified constraints in practice. The performance is studied under the limiting factors of cell load, user mobility, the number of users per cell, data traffic characteristics, and the LTE standards constraint of using a single modulation and coding scheme (MCS) across assigned resource blocks. To address the single MCS restriction, a dynamic Proportional Fair (PF) scheduling algorithm is developed to achieve optimal allocation under this constraint. The gain either in signal-to-interference-plus-noise ratio (SINR) or cell throughput achieved from these algorithms is statistically quantified using detailed LTE system level simulations.
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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 | 0.002 |
| 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