Comparisons of link-adaptation-based scheduling algorithms for the WCDMA system with high-speed downlink packet access
Bibliographic record
Abstract
The wideband code division multiple access (WCDMA) system with high-speed downlink packet access (HSDPA) is an important next-generation wireless system. By adopting adaptive modulation, efficient scheduling, and hybrid automatic repeat request technologies, it can support data rates of up to 10 Mb/s in the mobile cellular environment. Among these techniques, the scheduling algorithm plays a key role in realizing the HSDPA concept. A good scheduling algorithm should consider all the important factors, including channel impact, delay issues, and fairness. In this paper, a fairness index is adopted to examine the fairness performance of current link-adaptation-based scheduling algorithms, including the maximum carrier-to-interference (C/I), round-robin, proportional fair, and exponential rule schedulers. It is found that when multi-type services are supported, the fairness performance of current scheduling algorithms, including the round-robin scheduler, can be further improved even though the round-robin scheduler is viewed as the scheduler of the greatest fairness. Thus, a new scheduling algorithm, namely the queue-based exponential rule scheduler, is developed. Through simulations, it is shown that in the context of multi-type services the fairness performance of the queue-based exponential rule scheduler can surpass that of all the other schedulers in the time-multiplexing fashion, while maintaining good throughput and delay performance.
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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 | 0.006 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".