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Record W2155947017 · doi:10.1017/s1759078713000056

Application of embedding dimension estimation to Volterra series-based behavioral modeling and predistortion of wideband RF power amplifier

2013· article· en· W2155947017 on OpenAlex
Bilel Fehri, Slim Boumaiza

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

VenueInternational Journal of Microwave and Wireless Technologies · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPredistortionVolterra seriesWidebandAmplifierRF power amplifierLinearizationComputer scienceBehavioral modelingBandwidth (computing)Nonlinear systemControl theory (sociology)Electronic engineeringTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper expounds the systematic modeling of the behavior of radio frequency ( RF ) power amplifiers (PAs) exhibiting nonlinear, dynamic behavior. The approach begins with an analysis of the PA output signal to deduce the minimum embedding parameters required to accurately model its response, particularly the nonlinearity order and memory effects depth. The knowledge of the RF PA is then exploited in limiting the number of kernels consequently addressing the complexity of the Volterra series which has been the key hindrance to its wider practical adoption. In the proposed Volterra series model, performance is assessed and compared to memory polynomial model and dynamic deviation reduction Volterra models when used to linearize different high-power amplifiers driven with wideband signals of bandwidth up to 40 MHz. Significant linearization performance is achieved using a reduced number of kernels.

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 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: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.416

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 communication0.0000.000
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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