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Record W2131761622 · doi:10.1109/icecs.2008.4674819

Weighted criteria for RF power amplifiers identification in wide-band context

2008· article· en· W2131761622 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPredistortionAmplifierContext (archaeology)W-CDMAComputer scienceIdentification (biology)Radio frequencyPolynomialRF power amplifierElectronic engineeringPolynomial and rational function modelingPower (physics)AlgorithmCode division multiple accessControl theory (sociology)MathematicsTelecommunicationsEngineeringBandwidth (computing)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Modeling accurately RF power amplifiers (PAs) is an essential first step for predistortion and identification purposes. In WCDMA context, we prove that inaccurate results of identification are achieved because of the inadequacy of the classical criteria of least squares fitting to the WCDMA input pdf. The performances of the identification can be enhanced through a weighted criteria taking into account AM/AM dispersion for low input amplitudes. In this paper, the weighted criteria is validated for 1- carrier WCDMA input and two RF PAs, considering polynomial memoryless model and multibranch polynomial model.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.673

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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

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