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Record W2100241146 · doi:10.1109/wocn.2005.1435986

An enhanced distributed channel access deficit round-robin (EDRR) access scheme for IEEE 802.11 wireless networks

2005· article· en· W2100241146 on OpenAlexaff
C.J. Powell, Hussein Alnuweiri, Panos Nasiopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInter-Access Point ProtocolIEEE 802.11e-2005Computer scienceComputer networkIEEE 802.11uService setIEEE 802.11sIEEE 802Wireless Multimedia ExtensionsWireless networkQuality of serviceIEEE 802.11IEEE 802.1XChannel (broadcasting)WirelessScheme (mathematics)IEEE 802.11r-2008IEEE 802.11b-1999Wireless distribution systemWi-FiWi-Fi arrayTelecommunicationsWireless mesh network

Abstract

fetched live from OpenAlex

IEEE 802.11 wireless LAN is the most popular and most widely deployed Wi-Fi technology in the world. With the explosion in use of wireless networks comes the demand for them to support the latest technologies. Task group E of the 802.11 standard is working on a standard that adds quality of service enhancements in the 802.11 MAC layer by using traffic flows and prioritizing traffic according to what type of application it belongs to. There are, however, some weaknesses in the design that is currently being proposed by the 802.11 standards committee. In this paper, we propose enhancements to the IEEE 802.11 draft standard in an attempt to provide improved quality of service for all traffic priorities. Through our simulations we are able to show that we can do so without negatively affecting the performance of any of the wireless stations in the network or the traffic flows that they contain. Our results show that the proposed scheme provides an attractive and effective mechanism for enhancing the IEEE 802.11 standard.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0030.005
Open science0.0050.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.327
Teacher spread0.289 · 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 teacher head, not a consensus.

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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