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Record W2121486093 · doi:10.1109/lcn.2007.14

A Novel MIMO-Aware Distributed Media Access Control Scheme for IEEE 802.11 Wireless Local Area Networks

2007· article· en· W2121486093 on OpenAlexaff
Dan J. Dechen, Khalim Amjad Meerja, Abdallah Shami, Serguei Primak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsWestern University
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsMIMOComputer scienceComputer networkMedia access controlThroughputNetwork allocation vectorScheme (mathematics)Wireless distribution systemMultiple Access with Collision Avoidance for WirelessMulti-user MIMOWirelessAccess controlInter-Access Point ProtocolIEEE 802.1XNetwork packetWireless networkChannel (broadcasting)IEEE 802.11e-2005IEEE 802.11Wi-FiWi-Fi arrayTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper proposes a new distributed media access control (MAC) scheme to improve the network performance of Multiple-Input Multiple-Output (MIMO) wireless systems. In particular, this novel MAC scheme efficiently schedules two simultaneous transmissions between wireless stations (each equipped with three antennas) that are located in a single collision domain. Along with weighted nulling and intelligent packet fragmentation, this MAC scheme is capable of providing more efficient utilization of channel resources. The proposed MIMO-aware MAC scheme is compatible with the IEEE 802.11 standard. Detailed simulations are carried out to study the performance of the proposed scheme. The performance of the MIMO-aware MAC scheme is also compared with a recently proposed MIMO MAC scheme in the literature which is compatible with the IEEE 802.11 standard. Comparisons reveal that the proposed MIMO-aware MAC scheme achieves better throughput and delay performance under both saturated and unsaturated conditions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.289
Teacher spread0.256 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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