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Record W2169023753 · doi:10.5539/mas.v6n2p2

An Optimized Highly Efficient RF Power Amplifier for WLAN System Application

2012· article· en· W2169023753 on OpenAlexvenueno aff
Mohammed Hussein Ali, Chandan Kumar Chakrabarty, Mithaq H. Raheema

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsnot available
Fundersnot available
KeywordsAmplifierRF power amplifierLinearityPower (physics)dBmOperational transconductance amplifierPower-added efficiencyComputer scienceElectronic engineeringLinear amplifierElectrical engineeringTelecommunicationsOperational amplifierBandwidth (computing)EngineeringPhysics

Abstract

fetched live from OpenAlex

The class-AB/ F power amplifier (PA), Class F is commonly understood to be a switching PA, but in fact, it can also be a transconductance PA, depending on how hard the active device is driven. This paper presents the design a multistage class AB/Inverse F power Amplifier with high power added efficiency (PAE) and acceptable linearity for the WLAN applications. The effectiveness of the proposed controller has been verified by comparing proposed method with another methods using simulation study under a variety of conditions. The proposed circuit operation for a WLAN signal delivers a power-added efficiency (PAE) of 43.9% is measured at 32.1-dBm output power and linearity of inverse class F displays the simulation result of 1-dB compression output power of 17.5 dBm at -22.4 dBm input power. Finally, the proposed PA is show a good and acceptable result for the WLAN system.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.012
GPT teacher head0.243
Teacher spread0.232 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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