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Record W2024778847 · doi:10.1109/ccece.2008.4564855

Delay-sensitive and channel-aware scheduling in next generation wireless networks

2008· article· en· W2024778847 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceComputer networkQueuing delayTransmission delayNetwork packetProcessing delayScheduling (production processes)Fair queuingQueueing theoryNetwork delayWireless networkQueueEnd-to-end delayReal-time computingRound-robin schedulingDistributed computingWirelessQuality of serviceDynamic priority schedulingEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper studies and develops efficient traffic management techniques at the base station of future multi-service IP-based network. The proposed scheduler does not only consider the delay requirements of each traffic class in a static manner, but also the instant delay margin of each packet. This is due to the fact that the instant delay margin is significantly more meaningful than the delay requirement because it can quantify how urgent the packet is, and thus can timely and exactly determine the queuing priority that should be given to the packet. Besides, a novel queue architecture which allows the integration of delay margin based scheduling and user channel based scheduling is proposed. Queue length and packet delay survivor functions of proposed algorithms are studied by simulations in a typical wireless access network.

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 score0.518
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 integrity0.0000.001
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 head0.022
GPT teacher head0.184
Teacher spread0.162 · 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