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Record W4242412790 · doi:10.1002/wcm.910

Per‐user service model for opportunistic scheduling scheme over fading channels

2009· article· en· W4242412790 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

VenueWireless Communications and Mobile Computing · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRayleigh fadingComputer scienceScheduling (production processes)FadingMarkov processFinite stateQueueMarkov chainComputer networkBase stationMarkov modelAlgorithmReal-time computingMathematical optimizationChannel (broadcasting)MathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract In this paper, we propose a finite‐state Markov model for per‐user service of an opportunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading channels as finite‐state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a finite‐state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets. Copyright © 2009 John Wiley & Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
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 score0.648
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.030
GPT teacher head0.281
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