A novel QoS‐aware MAC protocol for voice services over IEEE 802.11‐based WLANs
Bibliographic record
Abstract
Abstract With the pervasive growth in the popularity of IEEE 802.11‐based wireless local area networks (WLANs) worldwide, the demand to support delay‐sensitive services such as voice has increased very rapidly. This paper provides a comprehensive survey on the medium access control (MAC) architectures and quality of service (QoS) provisioning issues for WLANs. The major challenges in providing QoS to voice services through WLAN MAC protocols are outlined and the solution approaches proposed in the literature are reviewed. To this end, a novel QoS‐aware wireless MAC protocol, called hybrid contention‐free access (H‐CFA) protocol and a call admission control technique, called traffic stream admission control (TS‐AC) algorithm, are presented. The H‐CFA protocol is based on a novel idea that combines two contention‐free wireless medium access approaches, that is, round‐robin polling and time‐division multiple access (TDMA)‐like time slot assignment, and it increases the capacity of WLANs through efficient silence suppression. The TS‐AC algorithm ensures efficient admission control for consistent delay‐bound guarantees and further maximizes the capacity through exploiting the voice characteristic that it can tolerate some level of inconsecutive packet loss. The benefits of the proposed schemes are demonstrated in the simulations results. Copyright © 2008 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".