Robust scheduling algorithm for guaranteed bit rate services
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel packet scheduling algorithm to overcome detrimental effects of channel impairments on the quality of service of delay-sensitive Guaranteed Bit Rate (GBR) services. The proposed algorithm prioritises the packets that require retransmission of Hybrid Automatic Repeat Request (HARQ) users compared to the packets of new users. The packets of new users are scheduled according to the Channel Quality Information (CQI), average throughput and packet delay information. Computer simulations have demonstrated that the proposed algorithm has 22.7% system capacity improvement over a well-known algorithm. It also tolerates for up to 200% delay of CQI and reduces the uplink signalling overhead by 150% compared to the well-known algorithm without compromising the quality of service requirements of the GBR services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it