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Record W3025653889 · doi:10.1109/access.2020.2994318

Efficiency Analysis of Concurrently Driven Power Amplifiers

2020· article· en· W3025653889 on OpenAlexafffund
Qi Tian, Songbai He, Biao Hu, Chang Liu, Xuekun Du, Yulong Zhao, Mohamed Helaoui, Wenhua Chen, Fadhel M. Ghannouchi

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsTone (literature)AmplifierSIGNAL (programming language)Computer sciencePower (physics)Nonlinear systemAmplitudeElectronic engineeringPhysicsTelecommunicationsBandwidth (computing)Engineering

Abstract

fetched live from OpenAlex

In this work, the properties of a two-tone signal driven concurrent power amplifier (CPA) are analyzed extensively. Firstly, the characteristics of two-tone signals are discussed to explain the nonlinearity and efficiency of two-tone signal driven CPA. Secondly, a method of how to solve the efficiency of a two-tone signal driven CPA is proposed in this paper with a detailed calculation procedure and complete theory. Finally, a general empirical expression to calculate the drain efficiency (DE) of CPA versus the frequency ratio and the amplitude ratio of the two carriers is proposed. Simulation results and experimental verifications are given to validate the proposed analytical formulation to predict the efficiency of CPAs when driven with balanced or imbalanced two-tone signals.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.291
Teacher spread0.255 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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