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Record W2145326248 · doi:10.1109/ccece.2007.209

Multi-Branch Polynomial Model with Embedded Average Power Dependency for 3G RF Power Amplifiers

2007· article· en· W2145326248 on OpenAlex
Oualid Hammi, Slim Boumaiza, Fadhel M. Ghannouchi

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
KeywordsAmplifierPower (physics)PolynomialRF power amplifierRange (aeronautics)Dependency (UML)Computer scienceMathematicsControl theory (sociology)Electronic engineeringEngineeringTelecommunicationsBandwidth (computing)PhysicsMathematical analysis

Abstract

fetched live from OpenAlex

In this paper, a study of the of multi-branch polynomial model performance for RF power amplifiers operating over a wide input power range is presented. For this purpose, a 100-watts peak power amplifier designed for 3G applications is characterized over a 12 dB average input power range. A multi-branch polynomial model is derived at each average power level. The models' accuracy in predicting the PA's output spectrum under average input power mismatch is evaluated. Then, the model's parameters variation versus the average input power is considered. It is shown that there is no straightforward analytical function that might take into account for the parameters variation with respect to the average input power level. Accordingly, a complexity reduced multi-branch model with embedded average power dependency is proposed. The model coefficients are stored into memory banks, and the adequate coefficients are loaded depending on the average input power level.

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 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 score0.789
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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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