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Record W2076769086 · doi:10.1109/chinacom.2006.344780

Capacity Analysis of Enhanced MAC in IEEE 802.11n

2006· article· en· W2076769086 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBottleneckComputer networkComputer scienceInter-Access Point ProtocolNetwork allocation vectorThroughputIEEE 802IEEE 802.11Wireless distribution systemIEEE 802.11sProtocol (science)Wireless lanWireless networkIEEE 802.1XIEEE 802.11e-2005WirelessIEEE 802.11uWi-FiTelecommunicationsQuality of serviceEmbedded systemWi-Fi array

Abstract

fetched live from OpenAlex

An analytical model for studying the access point (AP)-bottleneck effect of infrastructure wireless local area networks (WLANs) is developed. We extend the analytical model to investigate the performance of some enhanced MAC mechanisms in IEEE 802.11n. Two new aggregation schemes are also proposed and compared with previous schemes. We show that these MAC enhancements can effectively improve the network capacity by not only reducing the protocol overheads, but also smoothing the AP-bottleneck effect. Voice capacity of an IEEE 802.11n WLAN using different MAC mechanisms are presented as well.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.757
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.244
Teacher spread0.229 · 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

Quick stats

Citations12
Published2006
Admission routes1
Has abstractyes

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