A token-based scheduling scheme for WLANs supporting voice/data traffic and its performance analysis
Bibliographic record
Abstract
Most of the existing medium access control (MAC) protocols for wireless local area networks (WLANs) provide prioritized access by adjusting the contention window sizes or inter-frame spaces for different traffic classes. Those MAC protocols can only provide statistical priority access and limited service differentiation. In this paper, a novel token-based scheduling scheme is proposed for a fully-connected WLAN that supports both voice and data traffic. The proposed scheme can provide guaranteed priority access to voice traffic and, at the same time, provide more precise and quantitative service differentiation for data traffic, which provides great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can guarantee a small delay for voice traffic. For data traffic, it can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with a contention based scheme and a centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (in the contention based scheme) and the polling overhead (in the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".