Packet scheduling in 3.5G high-speed downlink packet access networks: breadth and depth
Bibliographic record
Abstract
Forecasts for emerging mobile device markets anticipate that bandwidth will be squeezed by demanding applications like multimedia on demand. This will spur the need for data rates beyond what the upcoming 3G wireless cellular systems such as UMTS can offer. To boost the support for such high data rates, HSDPA, labeled as a 3.5G wireless system, has been introduced in Release 5 of UMTS technical specifications. HSDPA is a definite step toward meeting the "anywhere, anytime, and in any form" 4G communication concept. HSDPA promises a peak data rate of up to 10 Mb/s, five times larger than the data rate offered by 3G systems. In order to support such high data rates, HSDPA relies on many new technologies, among which is packet scheduling. In this article we provide breadth and depth related issues of packet scheduling in HSDPA, discuss state-of-the-art HSDPA scheduling algorithms in terms of their objectives, advantages, and limitations, and suggest further research issues that need to be addressed. In addition, we propose a packet scheduling algorithm for data traffic in HSDPA. Simulation results demonstrate the effectiveness of the proposed algorithm
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".