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Record W2808256508 · doi:10.1109/arftg.2007.8376235

An integrated nonlinear behavior modeling system for RF power amplifiers/transmitters

2007· article· en· W2808256508 on OpenAlexaff
Taijun Liu, Slim Boumaiza, Fadhel M. Ghannouchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsAmplifierTransmitterNonlinear systemElectronic engineeringBehavioral modelingRF power amplifierComputer scienceTime domainSIGNAL (programming language)Frequency domainPower (physics)Radio frequencyEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes an integrated nonlinear behavior modeling system for RF power amplifiers/transmitters. The system includes several sub-routines such as: a) Signal generation/acquisition module used to feed the device under test with a modulated signal and to capture its output signal. b) Modeling module that first estimate and correct for the DUT delay and then identify the parameters of a given behavioral model (reverse or forward). c) Post-processing module which includes a time-domain, frequency domain and power domain validation features. It is also compatible with system level simulation tools such as Agilent-ADS and Simulink. Different dynamic nonlinear models and a variety of model identification algorithms can be applied to build the dynamic nonlinear behavior model of the power amplifier/transmitter.

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 categoriesMeta-epidemiology (narrow)
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 score0.797
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 integrity0.0000.000
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 head0.018
GPT teacher head0.263
Teacher spread0.246 · 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.

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

Citations1
Published2007
Admission routes1
Has abstractyes

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