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Record W1989852134 · doi:10.1177/0037549705060238

Simulation and Performance Evaluation of a Public Safety Wireless Network: Case Study

2005· article· en· W1989852134 on OpenAlexaff
N. Cackov, Junde Song, B. Vujičcić, S. Vujičcić, Ljiljana Trajković

Bibliographic record

VenueSIMULATION · 2005
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsSimon Fraser University
FundersDefense Advanced Research Projects Agency
KeywordsComputer scienceNetwork simulationSample (material)WirelessComputer networkWireless networkNetwork traffic simulationReal-time computingSimulationNetwork traffic controlTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

In this article, the authors describe simulation and performance evaluation of a deployed radio communication network operated by a public safety agency. The network consists of a central site and multiple cells.Each cell has a finite number of available radio channels. The network is circuit-switched. Hence, the system utilization is a time and space distribution of the number of concurrent calls. To determine the traffic load variations, the authors analyze activity data from sample weeks in 2002 and 2003. They simulate the network by using the OPNET simulation tool and a newly developed network simulator named WarnSim. Simulation models are based on the collected activity data and are used to evaluate the utilization of system resources and to locate network bottlenecks.

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)0.004
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.338
Teacher spread0.267 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

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