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Record W2144027420 · doi:10.1109/iscc.2009.5202311

Enhancements to IEEE 802.11 DCF collision avoidance based on MIMO Adaptive Spatial Channels Sharing

2009· article· en· W2144027420 on OpenAlexaff
Abduladhim Ashtaiwi, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceDistributed coordination functionMIMOCollision avoidanceComputer networkChannel (broadcasting)CollisionTransmission (telecommunications)Wi-FiWirelessIEEE 802.11Wireless networkTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

In this paper, the IEEE 802.11 Distributed Coordination Function (DCF) collision avoidance mechanism of Wireless Local Area Networks (WLANs) is enhanced based on Adaptive Spatial Channels Sharing (ASCsS) of Multiple Inputs Multiple Outputs (MIMO) transmission systems. Simply, instead of accessing the medium using all the spatial channels, to avoid collision the contending nodes use an optimal subset of the available spatial channels during the medium contention period. As the total number of concurrent used spatial channels is fewer than or equal to the spatial degree-of-freedom, receivers can still decode the transmitted singles of multiple medium access contenders. The performance of the ASCsS scheme demonstrates that the scheme further reduces medium access collisions and boosts the medium utilization.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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.

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

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
Published2009
Admission routes1
Has abstractyes

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