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Record W2140879911 · doi:10.1109/icmmt.2010.5525237

A simple method for tunable load impedance matching network of power amplifier

2010· article· en· W2140879911 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 institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsAmplifierPower-added efficiencyImpedance matchingInput impedanceRF power amplifierInductorCapacitorElectrical impedanceMaximum power principleLoad pullElectronic engineeringPower (physics)Computer sciencedBmElectrical engineeringControl theory (sociology)EngineeringVoltagePhysicsCMOS

Abstract

fetched live from OpenAlex

In this article, we proposed one simple method which achieves variable load impedance. By this method, without utilizing load pull tuner, the errors caused by various reasons, such as parasitic effect at the lumped components and active devices, can be corrected. We designed and fabricated one Class-AB amplifier to demonstrate our simple method by adjusting a shunt variable capacitor and inductor added at the input and output of the load matching network respectively. Before correcting the circuit, comparing the measured maximum PAE (Power Added efficiency) of the power amplifier (40.21% at 37.70 dBm output power) with the simulated PAE (52.96% at 40.35 dBm output power), the performance of this amplifier was not as what simulation predicted. After optimizing the circuit with our method, we obtained the maximum PAE reached 50.63% and the maximum output power increased to 40.10 dBm which more agree with the simulated result than the result before the correction.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.397
Threshold uncertainty score0.781

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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