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Record W2150156233 · doi:10.1109/icc.2008.32

Downlink Performance for Mixed Web/VoIP Traffic in 1xEVDO Revision A Networks

2008· article· en· W2150156233 on OpenAlexaff
Hanbing Sun, Carey Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceComputer networkQuality of serviceVoice over IPTelecommunications linkWeb trafficScheduling (production processes)Traffic generation modelNetwork packetQueueQueueing theoryErlang (programming language)Distributed computingReal-time computingEngineeringThe Internet

Abstract

fetched live from OpenAlex

Current 3G cellular networks, such as those based on 1xEVDO Revision A, have several mechanisms to provide quality of service (QoS) to network applications. In this paper, we investigate the performance for mixed Web/VoIP traffic on the downlink in 1xEVDO revision a networks. The impacts from the mixed traffic are studied by using link-level simulations. Our simulator is built based on a realistic system model that includes physical channel parameters, transmission formats, and channel scheduling algorithms, as well as hierarchical traffic models. User-perceived throughput and packet delay are explored. We present our observations and determine the Erlang capacity of the network. We carry out performance analysis using an equivalent queue approach, and discuss appropriate scheduling algorithms and call admission control mechanisms based on the simulation results.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.202
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2008
Admission routes1
Has abstractyes

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