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Record W1598893092 · doi:10.1109/ieee-iws.2015.7164608

Behavioral modeling of envelope tracking power amplifier using Volterra series model and compressed sampling

2015· article· en· W1598893092 on OpenAlexaff
Mohamed O. Khalifa, Abubaker Abdelhafiz, Andrew Kwan, Fadhel M. Ghannouchi, Oualid Hammi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersKing Abdulaziz City for Science and Technology
KeywordsAmplifierVolterra seriesRF power amplifierEnvelope (radar)Electronic engineeringPower (physics)Control theory (sociology)Computer sciencePower-added efficiencyLinear amplifierNonlinear systemEngineeringTelecommunicationsPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

Radio frequency power amplifiers operate most efficiently in the compression region and become less efficient as signal peak-to-average power ratio increases. This is due to the amplifier spending more time operating below peak power and, thus operating below its maximum efficiency. However, the use of the envelope tracking technique can dramatically increase the efficiency of RF transmitters. In this work, a Volterra series-based behavioral model with compressed sampling is developed for an efficiency optimized envelope tracking power amplifier. The proposed method reduces the total number of coefficients needed by the Volterra series model and enables accurate behavioral modeling of nonlinear envelope tracking power amplifiers. Experimental results carried on a GaN based power amplifier driven by a four-carrier 18 MHz WCDMA signal demonstrate the effectiveness of the proposed model and approach.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.447
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
Bibliometrics0.0000.000
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 head0.175
GPT teacher head0.319
Teacher spread0.145 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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