An Evaluation of the Proportional Fair Scheduler in a Physically Deployed LTE-A Network
Bibliographic record
Abstract
The Proportional Fair (PF) scheduler has been extensively studied in wireless communications research. Most of the research done, however, focuses on theoretical or simplistic simulations. In this paper, both theory and practical measurements for a PF scheduler are studied. Two data collections are conducted to verify the performance of the scheduler in an actual LTE-A network (small cells) environment. Allocated Physical Resource Blocks (PRBs) and throughput of each phone used in the data collection are estimated. Three different types of PF schedulers are implemented to predict user throughput. The results show that the scheduler maintains good fairness for both user throughputs and PRB allocation. Further it is shown that our results, derived from actual recorded data are different from those derived from simulation models presented in the literature [1] [2]. Similarly, the cell throughput and fairness values are dynamic and randomly distributed with the time in an actual LTE-A network in contrast to simulation models. From our study, we show that the generalize PF Scheduler performs more accurately to predict the user throughput values. It is concluded that this real-world LTE-A network study is more meaningful and valuable in enhancing the understanding of actual 4G and future 5G networks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".