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Optimal Fundamental Load Modulation for Class-X Harmonically Tuned Power Amplifier

2019· article· en· W3011669061 on OpenAlexaff
Yulong Zhao, Xiang Li, Chao Gai, Xuekun Du, Mohamed Helaoui, Fadhel M. Ghannouchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmplifierControl theory (sociology)TrajectoryHarmonicElectrical impedanceWaveformInput impedanceModulation (music)Fundamental frequencyPower (physics)Load pullComputer sciencePhysicsElectrical engineeringEngineeringAcousticsVoltageTelecommunicationsBandwidth (computing)

Abstract

fetched live from OpenAlex

This paper presents a mathematical model of the optimal fundamental load modulation design space for Class-X harmonically tuned power amplifiers (PAs). Starting from Class-X PA with maximally flat waveform, the optimal fundamental load trajectory that maintains constant high efficiency for different output power back-off level is obtained. Optimal fundamental load trajectory with different sets of second and third harmonic impedances are studied and verified with harmonic load-pull measurement. By carefully choosing the second and third harmonic load impedance, the optimal fundamental load trajectory can cross the real axis, which provides more flexibility in amplifier design. A 10W Cree GaN transistor, CG2H40010F, is measured at 800 MHz. The measured load-pull contours locate on the calculated optimal fundamental trajectory which validates the theory proposed in this work.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations7
Published2019
Admission routes1
Has abstractyes

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