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Record W2053861976 · doi:10.1002/mop.28642

Pseudo‐Zernike polynomials for numerically stable PA modeling using 3G+ signals

2014· article· en· W2053861976 on OpenAlexaff
Piyush Rawat, Meenakshi Rawat, N. Boulejfene

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPredistortionOrthogonalityZernike polynomialsBasebandOrthogonal polynomialsMathematicsPolynomialPolynomial and rational function modelingFunction (biology)Basis functionAmplifierStability (learning theory)AlgorithmGaussianProbability density functionApplied mathematicsMathematical analysisComputer sciencePhysicsTelecommunicationsOpticsGeometryStatistics

Abstract

fetched live from OpenAlex

ABSTRACT This article presents pseudo‐Zernike polynomials for numerically stable modeling and predistortion of high power amplifiers that, along with lesser coefficient dispersion, is capable of better model stability. Due to inherent properties of proposed basis function, orthogonality property of proposed basis function is independent of any phase rotation, which finds its use in baseband polynomial modeling. Moreover, in contrast to previously proposed orthogonal polynomial models, theoretically, presented model is independent from assumptions of Gaussian or uniform probability density function of the input signal unlike previous models. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2569–2572, 2014

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
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 head0.229
Teacher spread0.214 · 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 designBench or experimental
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

Citations1
Published2014
Admission routes1
Has abstractyes

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