Fair scheduling for real-time multimedia support in IEEE 802.16 wireless access networks
Bibliographic record
Abstract
Successful deployment of Broadband Wireless Access Networks such as WiMAX (IEEE 802.16) will be contingent on provisions for supporting multimedia traffic. In this paper, we review the quality of service features of access networks such as the 802.16 standard, and identify algorithms and schemes that are needed for supporting multimedia traffic in such networks. The 802.16 standard only specifies the features that should be implemented and leaves the design of a quality of service solution to developers. This includes the design of a mandatory scheduling framework. We present a comprehensive multimedia support framework based on the standard features. The framework specifies the architectures for the base station and the subscriber station, and contributes a number of algorithms for different service provisioning objectives. We use the concept of virtual packets to provide fair packet based centralized scheduling of uplink and downlink packets. The presented solution also provides algorithms for temporal and throughput fair scheduling in multirate physical layer of the 802.16 networks. An important part of the presented design is a multi-class fair scheduling scheme which is proposed for providing better delay performance for real time applications, while maintaining slightly longer term fairness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".