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Record W2095636673 · doi:10.1109/qshine.2005.46

QoS Mechanisms for the MAC Protocol of IEEE 802.11 WLANs

2005· article· en· W2095636673 on OpenAlexaff
J.R. Gallardo, Paúl Medina-Castro, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkComputer scienceQuality of serviceDistributed coordination functionInter-Access Point ProtocolIEEE 802.11IEEE 802.11e-2005Scheduling (production processes)IEEE 802.1XIEEE 802IEEE 802.11sNetwork allocation vectorNetwork packetService setMedia access controlAccess controlWirelessWireless networkWi-FiTelecommunicationsWi-Fi arrayEngineering

Abstract

fetched live from OpenAlex

There are two essential ingredients for any telecommunications system to be able to provide quality-of-service (QoS) guarantees: admission control (CAC) and service differentiation. In wireless local area networks (WLANs), it is essential to carry out these functions at the MAC level. The original version of IEEE 802.11 medium access control (MAC) protocol for WLANs does not include either function. The IEEE 802.11e draft standard includes new features to facilitate the provision of QoS, but no specific mechanisms are identified in it to make them possible. This paper introduces specific mechanisms into the relevant MAC protocol to avoid over saturating the medium (via CAC) and to decide how to assign the available resources (via service differentiation through scheduling). The main contributions of this work are a novel CAC algorithm for leaky-bucket constrained traffic streams, an original packet scheduling mechanism called DM-SCFQ, and a performance study of a WLAN including these features.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.027
GPT teacher head0.304
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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