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Record W4414198657 · doi:10.1109/access.2025.3609773

Systematic Design of a Compact Lumped-Element Outphasing Class-E Power Amplifier for HF Band

2025· article· en· W4414198657 on OpenAlexaff
K. H. Yusof, Farid Zubir, M. K. A. Rahim, Narendra Kumar Aridas, Jagadheswaran Rajendran, Noorlindawaty Md Jizat, P. Gardner, Thomas Johnson

Bibliographic record

VenueIEEE Access · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAmplifierMicrostripImpedance matchingHarmonicPower (physics)Electrical impedanceTransistorWirelessParametric statistics

Abstract

fetched live from OpenAlex

This paper presents a novel design methodology for an outphasing Class-E power amplifier operating at 27.12 MHz, specifically addressing limitations inherent to traditional outphasing combiners that utilize distributed microstrip transmission lines for harmonic termination. At MHz frequencies, conventional microstrip techniques become impractical due to excessively large physical dimensions. To overcome this, the proposed design employs an entirely lumped-element combiner network, eliminating the need for distributed microstrip harmonic terminations. A rigorous analytical framework is derived using two-port impedance matrices, ensuring simultaneous impedance matching at both peak and back-off power conditions. Parametric studies optimize component values, resulting in enhanced efficiency and stable output power across a broad range of outphasing angles. A prototype employing GaN-based transistors demonstrates high efficiency of 73.2% at peak output power and maintains above 50% efficiency at 6 dB output back-off, validating the effectiveness and practicality of the proposed lumped-element approach. This compact and efficient configuration offers significant advantages for MHz-band RF transmitters and wireless power transfer systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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