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Record W4392667142 · doi:10.1109/tcsii.2024.3376204

Linearization of Load Mismatched Power Amplifiers Using Reflection-Aware Augmented Polynomial Model

2024· article· en· W4392667142 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Technology Futures
KeywordsPredistortionAmplifierLinearizationPower (physics)Reflection (computer programming)Computer scienceNonlinear systemTransmitterInput impedanceElectrical impedanceRF power amplifierControl theory (sociology)Electronic engineeringElectrical engineeringEngineeringTelecommunicationsPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

Wireless transmitters are affected by the reflection caused by an impedance mismatch between the power amplifier’s (PA) output and the antenna’s input. Isolators can lessen the output mismatch of the PA, but they add the bulk to the transmitter. A reflection-aware augmented PA modeling and Digital Predistortion (DPD) technique are proposed to reduce the influence of the dynamic varying reflection due to the output-load mismatch and the PA’s nonlinearities. This Augmented model includes a term that characterizes the mismatched effect. The proposed DPD with a single set of coefficients is robust and can mitigate the dynamic varying output-load mismatch with the PA nonlinearity.

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 categoriesMeta-epidemiology (narrow)
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 score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.265
Teacher spread0.234 · 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