A Unified Framework for Adaptively Scheduling Hybrid Voice/Data Traffic in 3G Cellular CDMA Downlinks
Bibliographic record
Abstract
A unified framework for scheduling hybrid voice and data traffic is proposed for CDMA downlinks. We address the consistency of the framework as well as the distinctions of voice and data scheduling processes by discussing the common policy and individual requirements of both classes. An adaptive priority profile is designed in the scheduling algorithm based on queuing delay, required transmission power, and available transmission rate, which borrows the idea of composite metric from wired systems. With this design, the proposed algorithm accomplishes systems performance enhancement as a whole while retaining separate performance features without degradation. The uniformity of the proposed framework not only simplifies the implementation of the scheduling algorithms at base stations, but also is verified to be robust and resistant to various offered traffic load and variable service structure (voice/data proportion). Numerical conclusions show a system capacity gain of 4%-39% and traffic throughput improvement of 5%-26% most of the time over no scheduling systems. Compared with systems where no scheduling, or only data scheduling is employed, average outage probability is reduced by 50% and 94% respectively. The proposed framework is also tested to efficiently consume system resources by the approximate 100% power utilization capability, while no scheduling scenario exposes a 90% utmost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".