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Record W2023437471 · doi:10.1002/mop.20465

Analog predistorter using a Cartesian vector modulator structure

2004· article· en· W2023437471 on OpenAlexaff
Dong‐Hee Jang, Kyung‐Joon Cho, Sanghee Kim, Ji‐Yeon Kim, Jong‐Heon Kim, S.P. Stapleton

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPredistortionAmplifierElectronic engineeringBiasingCartesian coordinate systemBandwidth (computing)Automatic gain controlControl theory (sociology)EngineeringComputer scienceVoltageMathematicsTelecommunicationsElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Abstract We propose an analog predistorter that uses a Cartesian vector modulator structure. The operation principle of the proposed predistorter is analyzed for various gain and phase characteristics. The bandwidth performance of the predistorter is presented as a function of the dominant nonlinearity order. Experiments on several power amplifiers under various test signals were performed on the proposed analog predistorter. The experimental results demonstrate that the predistorter can easily control both the gain and phase characteristics via biasing the two IM generators. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 43: 343–348, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20465

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2004
Admission routes1
Has abstractyes

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