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Record W1979934649 · doi:10.2514/6.2007-3271

Modeling and Analysis of Multi-Port Power Amplifiers

2007· article· en· W1979934649 on OpenAlexaff
Xinping Huang, Mario Caron

Bibliographic record

Venue25th AIAA International Communications Satellite Systems Conference (organized by APSCC) · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsAmplifierPort (circuit theory)Computer sciencePower (physics)Transistor arrayCurrent sense amplifierElectronic engineeringElectrical engineeringTelecommunicationsEngineeringBandwidth (computing)PhysicsOperational amplifier

Abstract

fetched live from OpenAlex

(Abstract) In this paper, we present a generic model of multi-port power amplifier subsystems in the presence of device imperfections. This model allows quantifying the required component specifications to meet a given performance in terms of carrier-to- interference ratio or bit-error-rate degradation, or similarly to define the required performance of the calibration circuit. Examples of carrier-to-interference ratio and bit- error-rate performances of an 8-PSK and a 16-APSK communication system for different levels of device imperfections is presented to illustrate effects of device imperfections. It is concluded that at high frequencies such as Ka-band, a calibration technique is required to minimize performance losses in high-order modulation links. A novel calibration technique is briefly described.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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