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Record W2120479276 · doi:10.1109/mwsym.2005.1517132

Behavior modeling procedure of wideband RF transmitters exhibiting memory effects

2005· article· en· W2120479276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium Digest, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWidebandTransmitterAmplifierFinite impulse responseNonlinear systemElectronic engineeringComputer scienceTopology (electrical circuits)Filter (signal processing)Radio frequencyImpulse responseExponential functionBehavioral modelingControl theory (sociology)Bandwidth (computing)EngineeringMathematicsElectrical engineeringTelecommunicationsPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper proposes a new identification technique of Wiener or Hammerstein models applied to model the behavior of wideband RF transmitters. A dynamic exponential weighted moving average (DEWMA) algorithm was firstly applied to the raw signals sampled at the transmitter input and output to deduce the static AM/AM and AM/PM curves that are attributed to the memoryless nonlinear behavior. These memoryless curves were then utilized to deduce an intermediate set of data used in the identification of the dynamic linear sub-model. To model the frequency response and/or the memory effects, a finite impulse response (FIR) filter topology could be employed. The validation of the modeling approach was carried out using a 60-Watt GaAs FET push pull amplifier operating between 1930 MHz to 1990 MHz, which was applied by a two-carrier WCDMA.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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