A Novel Scheduling Algorithm for Video Traffic in High-Rate WPANs
Bibliographic record
Abstract
The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, MPEG-4 video streams are deemed to be a widespread traffic type. However, in the current IEEE 802.15.3 standard for media access control (MAC) of high-rate WPANs, the implementation details of some key issues such as scheduling and quality of service (QoS) provisioning have not been addressed. In this paper, we first propose a mathematical model for the optimal scheduling scheme for MPEG-4 flows in high-rate WPANs. We also propose an RL scheduler based on the reinforcement learning (RL) technique. Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT (Mangharam et al., 2004), EDD+SRPT (Torok et al., 2005), and PAP (Kim and Cho, 2005) scheduling algorithms in terms of a lower decoding failure rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".