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Record W2140787501 · doi:10.1109/tvt.2005.863426

A Node-Cooperative ARQ Scheme for Wireless Ad Hoc Networks

2006· article· en· W2140787501 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2006
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetransmissionComputer scienceJitterAutomatic repeat requestComputer networkNode (physics)ThroughputWireless ad hoc networkMarkov processWireless networkWirelessHybrid automatic repeat requestReal-time computingNetwork packetEngineeringMathematicsTelecommunications linkTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

In this paper, the authors propose a node-cooperative automatic repeat request (ARQ) scheme for wireless ad hoc networks, which is suitable for mobile wireless channels with high and correlated frame-error profile. An analytical model based on a two-state Markovian process is proposed to describe the behavior of the proposed retransmission scheme and to obtain its throughput, average delay, and delay jitter. The results of Monte Carlo simulations are included to demonstrate the efficacy of the proposed scheme and to verify the accuracy of the analytical models. Results show that a cooperation among a small number of nodes can significantly improve the performance of the retransmission process in terms of throughput, average delay, and delay jitter by reducing the average duration of retransmission trials.

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: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.986

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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