Analysis of throughput and fairness with downlink scheduling in WCDMA networks
Bibliographic record
Abstract
This paper is concerned with the throughput and fairness analysis in a downlink WCDMA network. The channel model is assumed to include path loss, lognormal shadowing and fast Rayleigh fading. The scheduling schemes investigated are (i) the round robin scheme, (ii) the maximum carrier-to-interference ratio (C/I) scheme, (iii) the proportional fair scheme, (iv) the maximum instantaneous signal scheme, and (v) the fading-based signal power scheme. By using an approximation of the probability density function of C/I, throughput and fairness expressions are derived, and a performance comparison among the five scheduling schemes is given. The results indicate that throughput and fairness performance of each scheduling scheme depends on mobile users' location. Tn general, the round robin scheme has the worst throughput performance as compared to the other four schemes. The proportional fair scheme and the fading-based signal power scheme can provide relatively better tradeoffs between the throughput and the fairness. The findings presented here are not only of fundamental theoretical value, but are also of practical interest to the designers of third-generation mobile communication systems based on WCDMA technology.
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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 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".