An Efficient Downlink-QoS Aware Scheduling Approach for Adopting Wyner-Ziv Code
Bibliographic record
Abstract
The next-generation cellular networks are facing several challenges. Precisely, according to recent statistics, by 2021, video traffic will represent 82% of the IP traffic. This demand increase will affect the users' quality-of-service (QoS). Accordingly, integrating appropriate distributed source coding (DSC) frameworks for mobile data traffic is crucially needed. Wyner-Ziv coding is considered a great solution to the high demand for video traffic. However, in LTE-A, the current standard of quality-of-service class identifier (QCI) gives a higher priority to the best-effort traffic, i.e., FTP and the rate-constrained traffic, i.e., VoIP. This current prioritization may not be suitable for the growing demands for the delay-constrained traffic, i.e., video traffic. Accordingly, in this paper, we investigate the effect of giving higher priority to the video-traffic on the QoS. To address this problem, we propose a QoS-aware downlink scheduler in a single-cell LTE network. In the proposed packet scheduler, we use a utility-based scheduling approach to study the effect of adopting Wyner-Ziv coding with prioritizing video traffic over other traffic types. Numerical results confirm that when using the Wyner-Ziv coding, changing the traffic priority to grant the video traffic the highest, will not impact the system's performance compared to other existing schemes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".