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Record W2162104643 · doi:10.1109/tcomm.2005.863788

Service differentiation in multirate wireless networks with weighted round-robin scheduling and ARQ-based error control

2006· article· en· W2162104643 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

VenueIEEE Transactions on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceScheduling (production processes)FadingComputer networkLink adaptationWireless networkAutomatic repeat requestHybrid automatic repeat requestWirelessPhysical layerWeighted round robinRound-robin schedulingQuality of serviceChannel (broadcasting)Fair-share schedulingTelecommunicationsTelecommunications linkMathematical optimizationMathematics

Abstract

fetched live from OpenAlex

The radio link-level delay statistics in a wireless network using adaptive modulation and coding (AMC), weighted round-robin (WRR) scheduling, and automatic repeat request-based error control is analyzed in this letter. WRR scheduling can be used for service differentiation similar to that achievable by using the generalized processor sharing scheduling discipline. The analytical framework presented in this letter captures physical and radio link-level aspects of a multirate multiuser wireless network (e.g., general fading model, AMC, scheduling, error control) in a unified way. It can be used for admission control and cross-layer design under statistical delay constraints. The analytical results are validated by simulations. Typical numerical results are presented, and their useful implications on the system performance are discussed.

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: Empirical · Consensus signal: none
Teacher disagreement score0.861
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
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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