Opportunistic scheduling for streaming users in high-speed downlink packet access (HSDPA)
Bibliographic record
Abstract
High-speed downlink packet access (HSDPA) achieves high data rates and high spectral efficiency by using adaptive modulation and coding (AMC) schemes and employing multi-code operation of CDMA. In this paper we present opportunistic algorithms for scheduling HSDPA users and selecting modulation and coding schemes that exploit channel variations to increase the probability of an uninterrupted media play-out. First we introduce a discrete event model for HSDPA system to transform the scheduling problem for providing an uninterrupted play-out to a feasibility problem that considers short term and long term quality of service (QoS) constraints. A methodology for obtaining a feasible solution is then proposed by starting with a so called stable algorithm that satisfies the long term QoS constraints (if possible with any scheduling policy). Next, we present stochastic approximation algorithms that adapt the parameters of the stable algorithm in a way that a feasible point for the short term QoS is reached within the feasibility region of the long term QoS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".