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Record W2141946107 · doi:10.1109/icc.2008.649

Performance Analysis of Weighted Proportional Fairness Scheduling in IEEE 802.16 Networks

2008· article· en· W2141946107 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkScheduling (production processes)IEEE 802Maximum throughput schedulingMax-min fairnessSpectral efficiencyQuality of serviceResource allocationDynamic priority schedulingRound-robin schedulingMathematical optimization

Abstract

fetched live from OpenAlex

In IEEE 802.16 networks, a subscriber station (SS) could be a single mobile user, a residence house, or an office building providing Internet service for multiple customers. Considering the heterogeneity among SSs which have different traffic load/demands, in the paper, we introduce the weighted proportional fair (WPF) scheduling scheme for best effort (BE) service in IEEE 802.16 networks to achieve the flexible and efficient resource allocation. Furthermore, an analytical model is developed to investigate the performance of WPF in terms of spectral efficiency, throughput, resource utilization, and fairness. Extensive simulations are conducted to illustrate the efficiency of the proposed scheme and verify the accuracy of the analytical model.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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