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Record W2017779981 · doi:10.1049/iet-map.2011.0099

Dual-band matching technique based on dual-characteristic impedance transformers for dual-band power amplifiers design

2011· article· en· W2017779981 on OpenAlexaff
Karun Rawat, Fadhel M. Ghannouchi

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmplifierElectronic engineeringImpedance matchingSmith chartMulti-band deviceComputer scienceGallium nitrideElectrical impedanceTransformerEngineeringElectrical engineeringBandwidth (computing)Materials scienceTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This study demonstrates a novel matching network synthesis technique that matches any arbitrary reflection coefficient seen by the active device at two uncorrelated frequencies to the standard 50 Ω load. The proposed matching network is fully transmission line based and, hence, can be used in high-power applications at higher frequencies. Unlike previously reported dual-band matching techniques, this work proposes and discusses various ways to achieve realisable solutions for arbitrary frequency ratios that account for fabrication limitations. The proposed synthesis approach is validated with the design and fabrication of a 10 W gallium nitride (GaN)-based class-AB amplifier for code division multiple access and Worldwide Interoperability for Microwave Access applications at 1960 and 3500 MHz. The amplifier has 59.8 and 55.1% drain efficiencies at saturation in the first and second bands, respectively.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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

Citations107
Published2011
Admission routes1
Has abstractyes

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