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Record W2162448260 · doi:10.1109/iscc.2009.5202321

Radio Access Network traffic generation for Mobile Switching Center

2009· article· en· W2162448260 on OpenAlexaff
Suliman Albasheir, Sofiène Tahar, Claude Gauthier, Jean Roussel Personna

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsResearch CanadaEricsson (Canada)Concordia University
Fundersnot available
KeywordsComputer scienceComputer networkCellular trafficUMTS frequency bandsTraffic generation modelMobile telephonyCellular networkTelecommunicationsRadio access networkServerPublic land mobile networkMobile radioMobile stationBase station

Abstract

fetched live from OpenAlex

One of the challenges faced by telecom companies is to provide robust and powerful servers that are capable to handle the great increase of the number of subscribers and to accomplish the heavy Internet-based applications that generate a tremendous traffic load. Companies evaluate their products' performance before releasing them to the market by applying a large amount of generated traffic to the telecom servers in order to measure their capability under traffic load; powerful solutions are hence needed for generating traffic and modeling various telecom protocols. In this paper, we propose a new traffic generator solution to load the mobile switching center (MSC) for the universal mobile telecommunications system (UMTS). This traffic generator loads the MSC through various mobile call scenarios such as location update, mobile call originating, mobile call terminating, and call clearing. We utilize the UML use case model to describe the functional behaviors of the traffic generator, and present the UML analysis model that provides the logical implementation of the functional behaviors of the proposed traffic generator.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.592

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 communication0.0010.001
Open science0.0010.000
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 head0.029
GPT teacher head0.280
Teacher spread0.252 · 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 teacher head, not a consensus.

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

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

Citations0
Published2009
Admission routes1
Has abstractyes

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