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Record W2103148242 · doi:10.1109/glocom.2010.5684057

Performance of IEEE 802.11 RTS/CTS with Finite Buffer and Load in Imperfect Channels: Modeling and Analysis

2010· article· en· W2103148242 on OpenAlex
Ahed Alshanyour, Anjali Agarwal

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 institutionsConcordia University
Fundersnot available
KeywordsRetransmissionComputer scienceMarkov chainQueueing theoryBuffer overflowQueueBuffer (optical fiber)Limit (mathematics)Markov processMarkov modelReal-time computingComputer networkMathematics

Abstract

fetched live from OpenAlex

Existing 2-D Markov chain models of IEEE 802.11 DCF mechanism are not capable to model the performance of the RTS/CTS access mode with finite buffer in imperfect channels due to the lack of adequate buffer and data retransmission limit models. This paper presents a discrete-time 4-D Markov chain model that integrates in addition to the data and control retransmission limits, the finite load, finite buffer capacity, and quality of the received data into a one model. Specifically, the additional two dimensions model the data retransmission limit and buffer capacity. Moreover, a single state, the idle state, is added to model the unsaturated condition. The 4-D model provides a new insight into QoS performance and queueing behavior of the IEEE 802.11 system. Simulation results also indicate that the analytical analysis is fairly accurate.

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: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.229
Teacher spread0.217 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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