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Bandwidth Performance Analysis of DLM PAs Including the Class-A/B/J Continuum

2019· article· en· W3010921435 on OpenAlexaff
Xuekun Du, Mohamed Helaoui, Fadhel M. Ghannouchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBandwidth (computing)WidebandMATLABAmplifierComputer scienceElectronic engineeringBiasingControl theory (sociology)Topology (electrical circuits)PhysicsVoltageEngineeringElectrical engineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new method is presented to analyze the bandwidth performance of power amplifiers (PAs) including the Class-A/B/J Continuum for dynamic load modulation (DLM). Due to the introduced biasing operation factor p, the analysis of bandwidth performance of DLM Class-J PAs can be extended to other modes, such as deep Class-AB mode. With the help of MATLAB software, the relationship between the drain efficiency (η) and output power back-off (OPBO) at the current generator plane (CGP) is demonstrated clearly. At the CGP, the proposed method indicates that when p = 0, the fractional bandwidth (FBW) of DLM PAs is improved from 73% to 141% compared with the results at the extrinsic plane (EP). When p is increased to 0.2, the FBW of DLM PAs can be improved from 141% to 156%. Furthermore, the proposed method in this work provides clear guideline to do a general analysis on the bandwidth performance of DLM PAs including the Class-A/B/J Continuum, and it also provides potential theoretical guidance for engineers to design wideband DLM PAs.

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 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: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.483

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 communication0.0000.000
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 head0.017
GPT teacher head0.225
Teacher spread0.207 · 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.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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