Towards an Efficient Reservation Algorithm for Distributed Reservation Protocols
Bibliographic record
Abstract
With the proliferation of wireless technologies and the convenience they offer, transporting Quality-of-Service (QoS) demanding traffic such as compressed video over wireless links becomes a trend and a challenging issue. Among many factors, Media Access Control (MAC) protocols play an important role in the network stack to ensure the QoS provisioning for multimedia applications and the efficient utilization of wireless channels. Various contention-based or contention-free MAC protocols have been proposed to solve these problems. In this paper, we model, analyze with an existing framework, and evaluate two reservation algorithms, subframe-fit and isozone-fit, proposed for distributed reservation protocols exampled by WiMedia UWB MAC. The models have been validated by extensive simulations using ns-2 and an MPEG-4 traffic generator. We further improve the system performance by introducing cross-isozone allocation and on-demand compaction to isozone-fit, and discuss how to leverage both contention-based and contention-free MAC protocols.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".